In response to anticipated challenges with urgent and emergency healthcare delivery during the early part of the COVID-19 pandemic, Yorkshire Ambulance Service NHS Trust introduced video technology to supplement remote triage and ‘hear and treat’ consultations as a pilot project in the EOC. We conducted a service evaluation with the aim of investigating patient and staff acceptability of video triage, and the safety of the decision-making process.This service evaluation utilised a mixture of routine and bespoke data collection. We sent postal surveys to patients who were recipients of a video triage, and clinicians who were involved in the video triage pilot logged calls they attempted and undertook.Between 27 March and 25 August 2020, clinicians documented 1073 triage calls. A successful video triage call was achieved in 641 (59.7%) cases. Clinical staff reported that video triage improved clinical assessment and decision making compared to telephone alone, and found the technology accessible for patients. Patients who received a video triage call and responded to the survey  were also satisfied with the technology and with the care they received. Callers receiving video triage that ended with a disposition of ‘hear and treat’ had a lower rate of re-contacting the service within 24 hours compared to callers that received clinical hub telephone triage alone .In this single NHS Ambulance Trust evaluation, the use of video triage for low-acuity calls appeared to be safe, with low rates of re-contact and high levels of patient and clinician satisfaction compared to standard telephone triage. However, video triage is not always appropriate for or acceptable to patients and technical issues were not uncommon. The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China in late 2019 and the subsequent global spread have resulted in a pandemic of coronavirus disease (COVID-19). The protection of healthcare staff has led to the introduction of measures designed to prevent transmission such as reduced face-to-face interactions. This has changed the way that healthcare is delivered across the world, with many healthcare providers introducing remote patient consultations by telephone and video .Prior to the pandemic, ambulance services managed around 6% of their 999 calls with telephone advice only, known as a ‘hear and treat’ response . In ordePrevious research has focused on the use of video consultations for routine outpatient appointments for chronic conditions and there is an absence of evidence of the effectiveness and satisfaction for patients and staff using this technology in an undifferentiated urgent or emergency case load . Recent We aimed to understand the impact of video triage by investigating patient and staff acceptability and the safety of the decision-making process.Our specific objectives were:To understand the experience of patients who receive video triage as part of an episode of care arising from a 999 call.To understand the experience of ambulance staff who are involved in a patient care episode which used video triage following a 999 call.The number of patients who are offered a video consultation but refuse or do not have the technical capabilityThe proportion of 999 calls that are closed as ‘hear and treat’ with and without video triageA comparison of 999 24-hour re-contact rates for video and non-video triaged calls that are closed with ‘hear and treat’ advice.To understand the outcomes of patients who are offered video triage as determined by:This service evaluation utilised a mixture of routine and bespoke data collection. We sent postal surveys to patients who were recipients of a video triage and had access to activity logs kept by clinicians who were involved in the video triage pilot.YAS provides 24-hour emergency and healthcare services for the county of Yorkshire in the north of England. The county has a population of approximately five million, spread over almost 6000 square miles of varied terrain, including isolated moors and dales, coastline and heavily populated urban areas. In 2019/2020, the emergency operations centre (EOC) in YAS received more than 1,054,575 calls and responded to 847,949 incidents by either sending clinicians to scene or providing assessment and advice over the telephone.Within the EOC at YAS, there is a clinical hub staffed by paramedics and nurses  who can provide telephone triage services to all categories of incident responded to by the service. In addition, they may provide telephone support to operational resources on-scene, to assist with decision making such as whether to convey patients to hospital. Due to the pandemic, a number of clinical hub and front-line operational staff were required to shield for 12 weeks. Implementation of remote telephone triage and consultation enabled these staff to continue working and this was supplemented by the addition of the video triage pilot.Calls eligible for video triage were initially restricted to adults aged 18 to 65 years that had been triaged as suitable for the clinical hub to review and manage. However, as the pilot continued, restrictions on calls were relaxed and extended to include category 2 and 3 calls and all age ranges. Patients under the age of 16 years were assessed with the consent of their parent or guardian.Video consultations, using either AcuRX™ or GoodSAM™, commenced on 23 March 2020 and information about the call was recorded by clinicians participating in the pilot. Clinicians also recorded their perception of the experience of video triage for patients and themselves, to determine acceptability and impact on the assessment process (Supplementary 1). This enhanced data collection commenced on 9 May 2020 and continued until the end of the pilot.To capture the patient experience, a YAS patient research ambassador was consulted on the creation of a brief postal survey (Supplementary 2), which was sent to recipients of a video consultation. Surveys were sent to patients between 11 June and 13 August 2020.In the absence of any validated patient-reported experience measures tailored to urgent and emergency care (UEC), we developed our own based on published indicators of ambulance service performance that are focused on patient outcomes: access; acceptability; decisions (e.g. to leave at home); satisfaction; professionalism; and holistic care  .The patient survey consisted of seven Likert-style questions, although survey respondents also had the option to make free-text comments. The free-text comments were stratified into broad themes and the most commonly recurring themes reported, with examples of each provided.To determine the 24-hour re-contact rate, data were collected from the YAS 999 Computer Aided Dispatch (CAD) system for all video triage calls. A comparison group, comprised of calls initially triaged as category 5 which were managed by the clinical hub , was also obtained. A re-contact was said to have occurred if a patient with the same NHS number, or same name, age, sex and incident location, called 999 in the 24 hours after the first call was recorded as being closed.No formal sample was calculated for this service evaluation and the analysis plan was predominantly descriptive. Likert-style question responses analysed using horizontal diverging stacked bar charts and clinician call activity were reported using counts and proportions stratified by video calling application (GoodSAM or accuRx). A sankey diagram was utilised to demonstrate change in initial and final triage category, and final call outcome.The differences in re-contact rates of category 5 calls between video calls and YAS-wide performance were reported as proportions, as were the rates of category 5 calls resulting in a ‘hear and treat’ outcome.Between 11 June and 13 August 2020, postal surveys were sent to 201 patients who were the recipient of a video triage call. Of these, 40/201 (19.9%) were returned prior to the evaluation closing. Patients that responded viewed the technology, the ambulance staff and the care planning favourably. In addition to the specific Likert questions, there was an opportunity for respondents to enter a free-text comment, which was utilised in 28/45 (62.2%) cases  cases . Video cThere were up to 713 responses from clinicians in relation to the Likert-style questions, with not all questions answered for every video call. Clinicians were only slightly more equivocal than patients about the impact of video triage in terms of improving their assessment and care of patients and the accessibility of the technology .The majority of calls selected for video triage by clinicians were initially triaged as category 5. However, as An analysis of category 5 calls shows that a higher proportion of calls that were initially triaged as category 5 remained so when a video triage was conducted compared to traditional telephone triage conducted by the clinical hub. Similarly, a higher proportion of calls had an outcome of ‘hear and treat’ when video triage was utilised . Re-contThe pandemic has seen a rapid expansion of telemedicine in the NHS, specifically video consultations. This has been achieved by centrally funded procurement, championing of the technology by NHSX and flexibility of conformance with information governance rules and legislation , 2020b. Randomised controlled trials in the use of video triage for clinical consultations have generally been underpowered and limited to carefully selected groups of patients in secondary care .Studies relating specifically to the ambulance service are small and while results are encouraging in terms of feasibility, the studies do not map out the economic benefit of such interventions and generally raise concerns about technical issues and/or acceptability of the technology by clinical staff . CliniciFrom the limited number of patient surveys returned, it appears that patients in our evaluation are satisfied with the use of video call technology. Clinicians reported that, where a call took place, video calling was acceptable compared to traditional methods of triage. In over 70% of calls, video consultation was perceived to be superior to telephone alone for patient assessment, decision making and patient care. Clinicians did however view video calls overall less favourably than patients.Unsurprisingly, the majority of calls  considered for video triage call were initially triaged as category 5, although higher categories of calls were answered as the pilot progressed and the eligibility rules were relaxed. There was some movement between initial and final triage category, with 2.1% of calls initially triaged as category 3 being downgraded to category 5, for example. However, conversely, substantial proportions of calls initially categorised as category 5 were upgraded to category 2  or 3 .While elsewhere, it appears that younger patients may be more likely to utilise the recent push for remote consultations , our evaRe-contact rate reporting was phased out of the Ambulance Quality Indicators in April 2018 although the last month in which all services were still reporting re-contact within 24 hours was July 2017 . Data frIt is difficult to compare historic re-contact rates with those occurring during a pandemic, but the re-contact rate for calls closed with ‘hear and treat’ advice by the clinical hub was 17.5%. Video triaged calls had a lower re-contact rate of 7.5%, although this is still higher than the aforementioned English mean re-contact rate.This project is a real-world evaluation and carried out in a single NHS Ambulance Trust so the results must be interpreted with caution. The patient and clinician satisfaction measures have not been validated and therefore may not be the most appropriate measures for this evaluation, but they provide a starting point for assessing the delivery of patient-centred care via video. In addition, the response to the patient survey was low and not all recipients of a video triage call received a survey, limiting the generalisability of these results.The source data for the video triage calls were self-reporting by participating clinicians and it is possible that cases were missed and affected by the clinician’s perception of calls that were considered appropriate for video consultation.Previous models have used the definition of patients attending ED or being admitted to hospital within 3 days of contact as being an incorrect ‘hear and treat’ or ‘see and treat’ response . We tookIn this single NHS Ambulance Trust evaluation, the use of video triage for low acuity calls appeared to be safe, with low rates of re-contact and high levels of patient and clinician satisfaction compared to standard telephone triage. However, video triage is not always appropriate or acceptable to patients and technical issues were not uncommon.This work uses data provided by patients and collected by the NHS as part of their care and support. The authors would also like to thank the clinicians of Yorkshire Ambulance Service who participated in the pilot and provided additional data to support this evaluation. Thanks also to Dr Fiona Sampson who kindly commented on a draft of this manuscript.FB, RC and DF conceived and designed the evaluation, RP, TL, LC and PW designed the data collection tools. RP undertook the analysis. All authors drafted the manuscript and contributed substantially to its revision. FB acts as the guarantor for this article.RP is on the editorial board of the BPJ but had no part in reviewing or considering this article. None of the other authors declare any conflicts of interest with respect to this service evaluation.The data for this evaluation are available on reasonable request.Formal Research Ethics Committee approval was not required for this work as it is a service evaluation. Approval for the evaluation to be conducted and confirmation that NHS REC was not required were obtained from the research and development department at Yorkshire Ambulance Service.None.


Great attention is paid to detecting video forgeries nowadays, especially with the widespread sharing of videos over social media and websites. Many video editing software programs are available and perform well in tampering with video contents or even creating fake videos. Forgery affects video integrity and authenticity and has serious implications. For example, digital videos for security and surveillance purposes are used as evidence in courts. In this paper, a newly developed passive video forgery scheme is introduced and discussed. The developed scheme is based on representing highly correlated video data with a low computational complexity third-order tensor tube-fiber mode. An arbitrary number of core tensors is selected to detect and locate two serious types of forgeries which are: insertion and deletion. These tensor data are orthogonally transformed to achieve more data reductions and to provide good features to trace forgery along the whole video. Experimental results and comparisons show the superiority of the proposed scheme with a precision value of up to 99% in detecting and locating both types of attacks for static as well as dynamic videos, quick-moving foreground items (single or multiple), zooming in and zooming out datasets which are rarely tested by previous works. Moreover, the proposed scheme offers a reduction in time and a linear computational complexity. Based on the used computer’s configurations, an average time of 35 s. is needed to detect and locate 40 forged frames out of 300 frames. Recently, recording videos using digital cameras, smartphones, and surveillance camcorders has become very easy and has been performed for many reasons in our everyday activities. Millions of videos are available every day, either uploaded over different internet sites or shared among social media. However, any video is easy to create or forge due to the widespread use of software video editing applications. Any editing video software can be used to tamper with videos such as Adobe Video Editor, Photoshop, Premiere by Adobe, and Windows Movie Maker, which are really good methods to easily edit video content, as anyone can edit the video files as it will be similar to the original content. These software applications have made forgery identification very difficult and have led to serious issues. Recently, detecting forged videos has gained great interest and has become a trending research topic compared to video authentication but authenticating the video contents may be unavailable all the time ,2.Digital video consists of a large group of sequential images, also known as frames, displayed in rapid succession to create the illusion of motion. Any malicious tampering in video content that alters its visual meaning is considered video forgery. Fast transition between scenes can be easily distinguished from forgery . Video FVideo forgery detecting methods are categorized into active and passive methods . Active Throughout the state-of-the-art methods, passive approaches work on video frames one-by-one in the spatial domain to detect signs of forgery. They compare all successive video frame features and depend on spatial correlation measures to prove the discontinuity of frame sequences. These features limit passive approaches performance in terms of detection time and accuracy, especially in the case of large video sizes with a low content variation. Recently, tensor data representation has been considered a trend computational approach to deal with large videos, it provides greater model fitting stability, easier to read and saves time .The method is based on comparing a limited number of orthogonal-features extracted from third-order tensor video decomposition;First, the whole video sequence is geometrically constructed into sub-groups, and each sub-group is mathematically decomposed into a group of third-order tensors. Then, instead of comparing all the frame/feature correlations, a group of arbitrarily chosen core sub-groups is orthogonally transformed to obtain essential features to trace along the tube fibers. Moreover, if a forgery is detected, these features can be used to localize the forged frames with high accuracy;The novelty of this paper is the great accuracy in detecting inter-frame forgeries. Hence, the geometric construction of successive video frames into third-order tensor tube fiber mode offers a great reduction in the number of pixels needed to trace forgeries;Checking one or two core sub-groups/third-order tensors of a limited number of pixels in the orthogonal domain is enough to detect frame discontinuities, compared with classic passive methods that examine the entire frame sequences. Additionally, this construction encapsulates the spatial and temporal features of successive frames into 2D matrices which can be manipulated and tested easily with high accuracy and less computational complexity.The offered approach in this paper develops a new inter-frame forgery passive approach that has high efficiency in respect to the achieved detection accuracy at minimum computational complexity. The main idea is as follows:The following paper structure is outlined as follows: Many important research developments have been made around digital video forensics. In this section, a summary of related research on passive approaches is introduced. Passive approaches trace video frames searching for three types of forgery: multiple/double compression, region tampering, and inter-frame video forgery. This proposed paper mainly considers the inter-frame forgery type in detail.Inter-frame video forgeries occur by inserting, deleting and duplicating frames in a video. Many studies that worked on inter-frame types faced problems such as accuracy and complexity of detecting and locating. Previous studies worked by comparing successive frames and found that they required a long time for video forgery detecting and locating regardless of forgery type. The most commonly used techniques in the studies were handcrafted methods  that depIn the case of frame duplication detection, Yang et al.  solved fFor the frame deletion cases, Liu et al.  detectedFor the case of frame deletion and insertion, Wang et al.  dependedFor the case of frame deletion, insertion and duplication cases, Bakas et al.  detectedSome studies tended to use deep learning methods in forgery detecting and locating but faced problems such as low accuracy, only detected forgery and some of them were forced to use labeled training sets as they used supervised learning. Long et al.  detectedThe first video forgery type is multiple/double compression which occurs when a video is to be manipulated in compressed format ,23. The According to the previous problems, the main challenge is the manipulation of large videos. The tensor structure provides an excellent method for representing many kinds of highly correlated data such as videos. It is used in many applications as in ,28,29. CThe proposed method undergoes passive approaches for the detecting and locating of inter-frame video forgeries. However, instead of spatially comparing the whole pixel correlation through all successive frames, a group of tracing orthogonal features ,32 is exIn the proposed method, mode-3 fibers are used. Since tube fibers preserve the continuity of the spatial and temporal video scene together with its correlation characteristics, in addition, the tracing features extracted from third-order tensor representation achieve high dimensionality reduction and exact continuity measure .The methodology of the proposed approach is illustrated in This phase is used to geometrically construct a third-order video tensor representation. As mentioned earlier, the main contribution in this phase is the great accuracy and reduction in computations, especially when dealing with large videos. T consisting of L frames, each has a dimension of H × W pixels, where H and W represent the total number of rows and columns, respectively. The video sequence T is divided into equal sub-groups P each of length equals L frames, each sub-group P is represented by a number of third-order tensors (mode-3 (tube fiber)) that is used to represent the flow of video data, which is a vector defined by fixing the first two indices  and varying the third index (number of frames), Here the 3D tensor is not represented by all frames, but the core P of the video frames that are always changed in the video. Practically, only one core sub-group P is chosen for 3D tensor representation to test video authenticity. Now, the mathematical expression that describes the above explanation is Equation (1):nP is the nth sub-group P, and N is total number of sub-groups of the input video. After dividing the video into sub-groups, core sub-groups are selected to be represented by several 3D tensors F < L, is the total number of frames of each 3D-tensor F decreases the accuracy of detecting forged frames increases, and vice versa. However, for the proposed techniques, it should not decrease by 10 frames or increase by 30 frames to get high detection accuracy, low computational complexity and to help in locating inter-frame forgeries as will be seen in the experimental results section. Finally, w and h are the selected number of columns and rows mt, where: h < H, and w < W and m = {1,2, …, M}, M is the number of all 3D tensors.Consider an input video Referring to T has dimensions of (192 × 192 pixels) × 300 frames, it can be divided into a total of nine P sub-groups, each with dimensions of (64 × 64 pixels) × 300 frame. The most important sub-groups can be chosen to be divided into a group of third-order tensors which are represented as a 2D matrix as in Equation (3) with dimensions of 20 × 4096 pixels. Here, it can be noted that the dimensions division process is arbitrary and corresponds to the nature of the scene of the suspected video.For example, if a total video container matrix mt is processed for feature extraction. There are many feature extraction methods used in forgery detecting and locating. Based on the previous studies, the three most effective methods used for extracting good features to trace are: Harris [Feature extraction is an important step for reducing data dimensionality, computational time and complexity. Each 2D matrix : Harris ,34, Gray: Harris  and Sing: Harris , In thismt as in Equation (3). Different detectors of the interest points were suggested and used based on the application field. The Harris detector, which is the fast, robust and rotation invariant, is commonly used in many computer vision applications that use the autocorrelation function to determine locations where the signal changes in one or two directions occur as in [In this step, Harris feature extraction is applied for each 2D matrix ur as in . The conur as in .mt included in each core sub-group P. Therefore, the autocorrelation matrix M for a given third-order tensor mt at point  can be calculated as in Equation (4):xt and yt are pixel intensity respective derivatives in the x and y directions at point . That is,xt and yt, while the diagonal entries are the squares of the respective derivatives and t is the element of mt. W can be uniform in the weighting function, but is more generally an isotropic and σ represents standard deviation. Circular Gaussian as in Equation (7):Harris feature extraction is applied for each tensor β be the M eigenvalues. These values provide a quantitative description of how the measure of autocorrelation changes its main curvatures in spatially. The image regions can be split into three groups according to the autocorrelation matrix eigenvalues: plain regions, edges, and corners. Note that the σβ product is sensitive to corners, while the σ + β sum is sensitive to both edges and corners. In addition, the trace and the determinant of a general diagonalizable matrix agree with the product and the sum of its eigenvalues:This gives greater weight to those values close to a local region’s center. Let Tr ) and Det ) to determine the corner response is attractive because it prevents the need for explicit decomposition of the M eigenvalue. The corner response is calculated using Equation (10):K is an empirically selected scalar value out of the range value . Corner points have high positive eigenvalues and thus a large response to the Harris measure. Thus, corner points that are greater than a specified threshold are recognized as local maxima of the Harris measure response:c, ycx)} is the corner point set, Corn is the Harris measure response computed at point , W is an 8-neighbor set centered around point  and tht is a specified threshold. Obviously, the number of Harris corner points identified depends on the threshold tht [Using hold tth .θ = 90° are constructed. Ninety degrees as video frames are arranged in tube tensor as Equation (12).k, l) and  express the locations of pixels with gray levels a and b. a, b represent the gray levels of pixel within a frame window separated by distance d and |{···}| represents set cardinality.Another different method for feature extraction is applied to improve the results of the Harris feature. Each sub-tube matrix p is processed for GLCM feature extraction. The Gray Level Co-occurrence Matrix (GLCM) is a method of texture feature extraction that is used effectively in various problems of image processing, such as segmentation, image recognition, classification, retrieval and texture analysis as in . The GLCDue to the nature of motions in video scenes, the required features must satisfy certain specifications. These features must provide stability, scaling properties and rotation invariance, to help trace those features through entire sub-tubes. SVD is a matrix factorization that has algebraic and geometric invariant properties. It has the ability to extract unique features for an image, which form a steady representation of image blocks. It has proven a great performance results in different applications ,35. SVD feature extraction is the method of robust and accurate decomposition of the orthogonal matrix. It is becoming increasingly common in the field of signal processing because of conceptual SVD and stability reasons. Image processing is an attractive algebraic transformation. In a minimally square sense, the SVD is the ideal matrix decomposition that stores the full signal energy into as few coefficients as possible. It is an effective and stable method of dividing the matrix into a set of linearly independent components, each with a contribution of its energy. It is a numerical method used in numerical analysis to diagonalize matrices. Due to its endless advantages such as maximum energy packing which is usually used in compression, ability to manipulate the image based on two distinctive subspaces of data and noise subspaces, it is an attractive algebraic transformation for image processing, which is commonly used in noise filtering and is also utilized in watermarking applications. mt, a singular value obtains the feature vectors of each part via SVD, which is given by Equation (13):In this paper, the SVD algorithm is deployed to third-order tensor. For each U and TV are the unitary matrices, and mX is the singular value of mt which is a diagonal matrix. The one-dimensional vector is formed from the diagonal elements of mt, and the vector can be expressed as mX = {m1x,…, mQx}m. X a feature vector of mt. mX for each mode-3 tube 2 D- matrix, the correlation coefficient between every two consecutive feature vectors is calculated using the standard Pearson correlation [mt and m + 1t tensors. Here, mth SVD feature of the mt tensor and P of the input video. For example, if a video consists of 300 frames, it is divided into several P according to its size, the chosen core P are divided into tensors and so be 15 tensors, each of which contains 20 frames. The correlation is calculated between every consecutive pairs of these 15 tensors to get 14 correlation values. These values are statistically averaged to get an average value of the correlation among tensors. Hence, a threshold value is calculated based on the obtained statistics and is used to detect video forgery. Thresholds vary in correspondence to the nature of each video. Using Chebyshev’s inequality [µ and σ are the mean and the standard deviation, respectively, of correlation distribution iR values of the total adjacent m tensors. Their mathematical representations are as follows:Here, the autocorrelation between consecutive tensors features is calculated. For example, after extracting SVD feature vector relation  as in Eqequality , this thAlgorithm 1 Forgery Type Determination.Input: Correlation values Rm where m = 1: M and Threshold. (14)–(15)   Output: Forgery type.   Begin1.   fordo2.     ifThresholdthen3.        4.           Forgery type is insertionelse ifThresholdthen5.        6.           Divide tensors with suspected values into Sub-Frames.if two suspected points are found then7.          8.              Forgery type is insertionelse9.           10.             Forgery type is deletionend11.           else12.        13.           No forgery end14.        end15.     end16.  For unknown data distribution, the lower bound for the threshold within a group of adjacent tensors can be determined by applying Chebyshev’s inequality. The correlation value computed from Equation (14) is compared with the computed threshold to define the type of forgery as insertion or deletion. Algorithm 1 illustrates the procedure of detecting.For more illustrations, let us consider a practical implementation for Algorithm 1. The tensor correlation distribution analysis of the original foreman video dataset is shown in To detect the frame deletion forgery case, the proposed method is applied to the forged dataset. For testing, we made 50 forged datasets for the deletion case. The correlation distribution analysis for the foreman dataset is shown in Recalling the proposed methodology, Fi . The feature vectors of each frame via SVD are obtained, which are given by:In the case of detecting forgery between two consecutive tensors, one tensor before and one tensor after are invoked, all these tensors are analyzed as frames (in our example 20 frames per tensor) to locate forgery in the video. The extracted frames are denoted by fS is SVD matrix of each frame in 3D-tensor, fY = {f1, …, YfBY} is one-dimensional vector as a feature of lf and f1Y and fBY are first and last feature values. fth and (f + 1)th subframes, zth SVD feature of the zth Sub-frames, and zth sub-frames. For example, if forgery is detected in tensors 5, 6 and 7, then these tensors are divided into frames from 100 to 160 and correlation is calculated between these frames to locate the position of forgery. According to the correlation values, the threshold is determined using the same Chebyshev’s inequality [After calculating singular values for each sub-frame in selected forged tensors, the correlation coefficient between every two consecutive sub-frames is computed. According to the correlation values, the threshold is determined to localize the forgery in the video. The same equation is applied in but between every consecutive frame as:equality  except tForgeries are simply localized from abnormal values in the inter tensor correlation distribution. However, for locating refinement, an inter-frame correlation distribution is applied. The distribution analysis for the foreman original video is shown in Algorithm 2 Forgery Location Determination.Input: Correlation values Rm where m = 1: M, Threshold, t which is tensor number. Output: Number of inserted or deleted Forged frames. begin1. fordo2.  if Forgery is detected at then3.   4.    Forgery type is insertion.5.    Divide tensors whose numbers are t − 1, t, t + 1, t + 2 into frames (from s to n).6.    Compute correlation between every two consecutive frames in fordo7.     if Two suspected values are found then8.      9.      Forgery location determinedend10.     else if forgery is detected at then11.   12.     Repeat steps 5, 6.if two suspected values are found then13.     14.      Forgery type is insertion and forgery determinedelse if one suspected value is found then15.     16.      Forgery type is deletion and forgery determinedend17.     else18.   19.    No forgeryend20.   end21.  end22. To evaluate the performance of the proposed scheme, a MATLAB computer simulation program  was developed for testing and validating several experiments. The computer configuration used in these experiments is described as follows: CPU: Intel(R) core (TM) i7-9750H CPU @2.60 GHZ ; Memory size: 16 GB RAM; OS: Microsoft Windows 10 ; the Coding: MATLAB R2018a;. The next subsections explain the tested dataset, the standard evaluation parameters. Finally, comparisons and discussion are introduced.Experiments on the proposed scheme are performed with a standard dataset consisting of eighteen video clips with a frame rate of 30 frames per second (fps), from the TRACE library, where each YUV sequence is either in Quarter Common Intermediate Format (QCIF) which is (176 × 144) format or Intermediate Format (CIF) which is (352 × 288) format . The tesManual forgeries are performed for frame insertion and deletion attacks on the above dataset. Videos are made using the ffmpeg tool which provides command-line or programmatic access to video and audio processing. The original video is first decomposed into individual frames, and then the forgery is performed by inserting or removing frames. In this paper, both forgery attack experiments are tested against small and large numbers of forged frames to test the robustness of the proposed scheme. Forged videos are created starting with 10 forged frames up to 50 frames. Forged videos are created using the Audio Video Interleave (AVI) extension in MATLAB R2018a and eventually, the forged videos are translated into the .YUV extension.TP is the true positive number which means that the forged video was detected as forged, TN is the true negative number which means that the original video was detected as original, FP is the number of false positive which means that the original video was detected as forged and FN is the number of false negatives which means that forged videos were detected as the original. To evaluate the validity of the scheme, three performance indices are considered: precision, recall and F1 score ,40,41 whThe proposed technique offers a great advantage of speeding up the detecting and locating process since it offers a great opportunity for parallel processing for different tensors at the same time instead of consecutive frame processing compared with state-of-the-art methods. This advantage has a great influence on the total time needed for forgery detecting and locating as will be discussed later. However, tensor size is linearly proportional to the number of computations.In this section, the proposed scheme is applied to the eighteen datasets depicted in For testing forgery attack detecting and locating, several experiments were conducted to trace the performance accuracy of the proposed scheme against the increase/decrease in the number of forged frames. As shown in The proposed scheme is tested and evaluated against the detecting and locating of deletion forgeries with different cases of deleted numbers of frames. As mentioned before, the SVD feature extraction method is used in deletion attacks as it achieves efficient results in insertion attacks. Comparison with the state-of-the-art is provided in order to compare the proposed scheme performance with different methods. We tested all methods on the same dataset. The method proposed by Yu et al.  detectedThe proposed method of this paper can detect insertion and deletion forgeries for a still background as well as dynamic background videos. The proposed method offers high accuracy in respect of the achieved precision at a minimum number of features compared with previous works. Recalling that the proposed tensor geometric structure provides a high reduction in computational time due to the small size of tensors and the possibility of processing tensors in a parallel manner rather than the consecutive approaches used in the state-of-the-art. However, it is difficult to compare experimental time with the state-of-the-art methods although they used the same dataset since different computer configurations together with different programmers’ skills are deployed. In this paper, based on the previously mentioned computer configurations used in these experiments, the average computation time per tensor is less than 2.2 s. Third-order tensor representation together with a good feature extraction method offered this great reduction. Considering the average computations time for previous methods ,17,42, aVideos are linear groups of highly correlated data that consume time and computational complexity. Recently, the most common methods for video compression represents such data on the basis of a geometric tensor representation. This paper proposed a low computational complexity scheme based on tensor representation and orthogonal tracing feature algorithms for detecting and locating insertion and deletion forgery in videos. Three different common tracing features were tested, evaluated, and compared to choose the outperforming one. Experiments and comparisons showed the superiority of SVD tube-fiber tensor construction in detecting and locating these two types of video forgeries. Different datasets of different characteristics were examined, and the proposed scheme was tested against the increase in the forged frame number. The proposed method performed efficiently for static as well as dynamic videos, quick-moving foreground items (single or multiple), zooming in and zooming out datasets. Experimental results showed that the proposed approach obtains effective accuracy with a high precision value of up to 99% and a reduction in time and computational complexity. Future research in this direction is still open, and it will include enhancing the detecting and locating process for more types of attacks.


Although a putative Mg2+ binding site was previously identified to reside in the aqueous pore in the first extracellular (E1) loop domain, the involvement of the N-terminal (NT) domain in the atypical response of Cx36 GJs to pH was shown to depend on intracellular levels of Mg2+. In this study, we examined the impact of amino acid substitutions in the NT domain on Mg2+ modulation of Cx36 GJs, focusing on positions predicted to line the pore funnel, which constitutes the cytoplasmic entrance of the channel pore. We find that charge substitutions at the 8th, 13th, and 18th positions had pronounced effects on Mg2+ sensitivity, particularly at position 13 at which an A13K substitution completely abolished sensitivity to Mg2+. To assess potential mechanisms of Mg2+ action, we constructed and tested a series of mathematical models that took into account gating of the component hemichannels in a Cx36 GJ channel as well as Mg2+ binding to each hemichannel in open and/or closed states. Simultaneous model fitting of measurements of junctional conductance, gj, and transjunctional Mg2+ fluxes using a fluorescent Mg2+ indicator suggested that the most viable mechanism for Cx36 regulation by Mg2+ entails the binding of Mg2+ to and subsequent stabilization of the closed state in each hemichannel. Reduced permeability to Mg2+ was also evident, particularly for the A13K substitution, but homology modeling of all charge-substituted NT variants showed only a moderate correlation between a reduction in the negative electrostatic potential and a reduction in the permeability to Mg2+ ions. Given the reported role of the E1 domain in Mg2+ binding together with the impact of NT substitutions on gating and the apparent state-dependence of Mg2+ binding, this study suggests that the NT domain can be an integral part of Mg2+ modulation of Cx36 GJs likely through the coupling of conformational changes between NT and E1 domains.Electrical synapses between neurons in the mammalian CNS are predominantly formed of the connexin36 (Cx36) gap junction (GJ) channel protein. Unique among GJs formed of a number of other members of the Cx gene family, Cx36 GJs possess a high sensitivity to intracellular Mg Gap junction (GJ) channels, formed of connexin (Cx) protein subunits, serve as intercellular communication pathways that enable the efficient propagation of electrical signals and the flux of small ions, amino acids, metabolites and peptides between cells . There aj), the voltage difference between cells  domain and in particular a charged residue, D47, predicted to be a pore-lining residue . These mutants were subcloned into pEGFP-N1 vector  and transfected into RIN cells using Lipofectamine 2000 . All experiments were performed with stable cell lines.Electrophysiological measurements were performed in RIN cells  transfected with WT or mutant variants of mouse Cx36 fused with enhanced green fluorescent protein (EGFP) attached to the carboxy terminus. Cells were cultured in RPMI 1640 medium, with j, was measured in cell pairs using the dual whole-cell patch clamp technique with EPC-7 (HEKA) or EPC-8 (HEKA) patch clamp amplifiers. Voltage and current signals were digitized using an A/D converter  and acquired and analyzed using custom-made software. Transjunctional voltages (Vj) were generated by stepping the voltage in cell-1 while keeping the voltage in cell-2 constant. Junctional current (Ij) was measured as the current change in cell-2. Patch pipette resistance was maintained below 3 MΩ in order to minimize the influence of series resistance on measurements of gj. Recording pipettes were pulled from borosilicate glass capillary tubes with filaments using P-97 micropipette puller .Electrophysiological recordings and fluorescence imaging were performed on cells grown on glass coverslips and transferred to an experimental chamber placed on the stage of an inverted microscope Olympus IX70  equipped with a fluorescence imaging system and a constant flow-through perfusion. Junctional conductance, g2, 1 MgCl2, 2 CsCl, 1 BaCl2, 5 HEPES, 5 glucose, 2 pyruvate, pH 7.4. Patch pipettes were filled with a solution containing (in mM): 130 CsCl, 10 NaAsp, 0.26 CaCl2, 5 HEPES, 2 BAPTA, 1 MgCl2, pH 7.3. To study the effect of [Mg2+]i pipette solutions contained different concentrations of MgCl2: 0.01, 1, or 5 mM. To chelate free Mg2+ ions in the cell we used 2 mM K2ATP in the pipette solution with 0.01 mM Mg2+. Maxchelator software was used to calculate free Mg2+ ionic concentrations. Solutions were adjusted for differences in osmolarity with the appropriate concentrations of CsCl.During recording, cells were perfused with an extracellular solution containing (in mM): 140 NaCl, 4 KCl, 2 CaCl2+ fluorescent indicator Mag-Fluo-4 (50 μM) was introduced into cell-1 of a cell pair through a patch pipette with modified standard pipette solution without MgCl2. Cell-2 was patched with the pipette containing standard pipette solution and 5 or 10 mM MgCl2. After breaking into cell-1, the fluorescence intensity (FI1) of Mag-Flou-4 rose to a steady state value. The patch in cell-2 was then opened and the changes in FI1 in cell-1 and gj were measured simultaneously. To measure gj, repeated, small amplitude voltage ramps (±10 mV) were applied.Fluorescence imaging was accomplished using an ORCA digital camera (Hamamatsu) and UltraVIEW  software. We used an excitation filter of 485, and emission filter of 530 to identify cell pairs expressing WT and mutant variants of Cx36. The same filters were used for fluorescence measurement of Mag-Fluo-4. For magnesium transfer studies, a MgStructural homology models of Cx36 and its mutants were built using as a template the structure of a Cx46/Cx50 GJ channel (6MHQ) obtained by cryo-EM . HomologThe electrostatic potentials of all the atoms in the protein were estimated using Adaptive Poisson–Boltzman solver (APBS). To convert PDB files into PQR as input to APBS, we used PDB2PQR server with a PARSE forcefield . PROPKA Introducing mutations adds features that may not be structurally accurate. To improve the accuracy of the system structure, we performed minimization and equilibration using GROMACS simulation software. The protein was inserted in a lipid bilayer membrane environment using the CHARMM-GUI membrane builder . The inn2+ action on GJ channels formed of Cx36. All the models were based on the assumption, that Mg2+ can bind to each of the apposing hemichannels and that each hemichannel can be described by a linear 3-state reaction scheme of the following form:We developed a series of different mathematical models to examine possible mechanisms of MgO and C denote open and closed states of a hemichannel, respectively. X denotes an intermediate state, which, could be either open or closed . In addition, we presumed that O→X and X→C transitions could be either Mg-independent or could reflect the binding of Mg2+ ions according to the law of mass action. This gives us eight different models to consider, depending on the assumptions about the intermediate state X, as well as O→X and X→C transitions , and another model (denoted by ×), which exhibits a redundant Mg-independent transition between two open states. This leaves us with five basic models, each describing somewhat different mechanistic scenarios. For example, in Model 1, a Mg2+ binding event occurs in a hemichannel in its open conformation, which remains open until a subsequent binding event occurs resulting in hemichannel closure, whereas in Model 5, a Mg2+ binding event occurs in a hemichannel in its closed conformation, with closure achieved through a Mg2+-independent gating event. For each of the five basic models, in addition to Mg2+ binding according to the law of mass action, we considered the Hill equation to describe Mg2+ binding, which can be interpreted as simultaneous binding of n Mg2+ ions, or more generally, expresses cooperative (n > 1), competitive (n < 1) or independent (n = 1) binding :The flux of Mgi  denotes the hemichannel in each of two apposing cells, cell-1 and cell-2. The constant P quantifies the permeability of a GJ channel to Mg2+ ions and gj (0 ≤ gj ≤ 1) is normalized junctional conductance, which can also be interpreted as the proportion of open channels between two cells. Because the inspected RIN cells were of similar sizes we made the simplifying assumption that both cells of a pair were of the same volume. Any differences in cell volumes should average out in different experiments and should not distort modeling results to any significant degree. In addition, to simulate experiments with highly asymmetric concentrations of Mg2+ in the pipette solutions, we subtracted a component Pleak⋅[Mg2 +]i to account for the leak of Mg2+ into the patch pipette in the recipient cell. The magnitude of this leak current parameter Pleak was estimated as described in  to ensure the stability of the calculations. To avoid parameter redundancy and overfitting, we presumed fluorescence intensity of the Mg2+ indicator was proportional to the intracellular Mg2+ concentration and the constant of proportionality should be reflected in parameter P, when simulated Mg2+ concentration curves were fitted directly to measured changes in fluorescence intensity over time.Parameter estimation was performed using the MATLAB global optimization toolbox. To estimate model parameters, the simulated changes in gWe applied the method of least squares for model fitting. That is, global optimization was performed to obtain model parameters which minimized the sum of squared errors (SSE):yi denotes data points obtained from the experiments while n is total number of experimentally observed values.Here In theory, inclusion of an additional parameter into a model can only result in a lower SSE during the model fitting, even if the addition carries little or no relevance. Thus, to avoid such overfitting we used the Akaike Information Criterion (AIC). For least squares model fitting problems, AIC can be estimated from the following equation:k denotes the number of model parameters.Here The value of AIC for a given model is useful only in comparison to AIC values of other models. That is, a model with the minimum value of AIC relative to all the others is considered the best. An increase in the number of model parameters raises the AIC value. Thus, such an addition is only justified if it significantly reduces the value of SSE. In this way, the use of AIC for model selection gives a preference for a more parsimonious model. In addition, it allows for a direct comparison among models which have different numbers of parameters.If two models are applied for the same experimental dataset, they can only differ in the values of the delta AIC (ΔAIC), which is defined as follows:As a rule of thumb, two candidate models are considered significantly different in fitting ability if the difference in their ΔAIC values exceeds 2 .wi), which are defined as follows:To compare different models we used the Akaike weights  and the final steady-state value . An index of the effect of Mg2+ was given by the ratio gj,ss/gj,init.The predicted structure of the Cx36 GJ channel based on homology modeling using the Cx46/Cx50 cryo-EM structure is shown in j,init  consistent with the roles of non-pore-lining residues in the maintenance of NT structure . Experiments were performed using the same protocol as described in 2+ in the other cell (cell-2) by establishing a second whole-cell recording with a Mg2+-containing pipette. This allowed simultaneous measurement of gj and changes in Mag-Fluo-4 fluorescence arising from the flux of Mg2+ through GJ channels. The effects of Mg2+ occurred over a time course of a few minutes during which time we saw no measurable changes in plaque size.Given that the NT domains constitute the cytoplasmic vestibules of a GJ channel pore, we wanted to evaluate whether the NT substitutions, particularly those that affected regulation by Mg2+ in the patch pipette for cell-2, 5 and 10 mM. The mean initial gj values . For model fitting, we used the 10 different 3-state models detailed in Methods. These models were fit to the electrophysiological and fluorescence imaging data presented in 2+ binding provided the best fit. The difference between ΔAICs of the best and second best model (Model 4 with the Hill equation) exceeds 2, which is considered significant. The ratio of the respective Akaike weights (∼1.91) indicates that Model 5 with the Hill equation has approximately a two times higher probability of being better than Model 4 with the Hill equation and is the best among all the considered models. In addition, Model 5 with the Hill equation provided the lowest SSE for independent validation data, which further strengthens our confidence that it is the most suitable candidate model.Data in The kinetic scheme of this model  is presented below:j-dependent. The second transition can be interpreted as the binding of Mg2+ ion(s) to the closed state of a hemichannel and subsequent stabilization of this closed state. The system of ODEs, describing Mg2+-binding and closure of a single hemichannel is presented below:The first transition of this model is Mg-independent, which can be interpreted as stochastic gating of a hemichannel that is Vi  denotes the hemichannel in each of two apposing cells, cell-1 or cell-2 and n is the Hill coefficient for the binding of Mg2+ ions. State variables [O]i, [C]i and [C−Mg2 +]i each denotes the probability that a hemichannel resides in an open, closed or Mg2+-stabilized closed state, respectively. These variables must be in the range  and satisfy the following conservation rule:Here, j can be expressed as gj = [O]1⋅[O]2. The estimated parameters of this model for WT and mutated Cx36 channels are presented in Based on the assumption that each apposing hemichannel can close the GJ channel pore, gj for E3Q and E8Q as compared to WT GJ channels mostly result from a decreased dissociation of Mg2+ ions from the putative binding site. As for WT Cx36, the estimated value of the Hill coefficient for both E3Q and E8Q variants was close to 1, which indicates the absence of either cooperative or competitive binding of Mg2+ ions. The values of parameter P indicate that E3Q exhibited an increased permeability to Mg2+ ions whereas E8Q exhibited a decrease compared to WT. The estimated parameters for A13K reflect two main distinguishing features. First, A13K was the only variant that resulted in a substantially reduced Mg2+ binding rate, as indicated by a very low value of k3, or more precisely, the reduced ratio k3/k4, compared to WT and the other variants. In addition, the estimated value of P indicates that A13K exhibits a more than 10-fold reduction in permeability to Mg2+ ions. H18K also exhibited reduced permeability to Mg2+, but similar in magnitude to E8Q. Compared to WT Cx36, however, H18K also showed an increase in stochastic gating activity at Vj = 0 mV  as well as a much lower value of the Hill coefficient. The latter may indicate a very low cooperativity for Mg2+ ion binding and could explain saturation of the Mg2+ effects on gj sensitivity at 5 mM. In addition, it would result in a lower sensitivity to high Mg2+ concentrations.The estimated values of model parameters suggest that the observed slower changes in gj and Mag-Fluo-4 fluorescence  over a 5 min time frame. Under symmetric 5 mM Mg2+conditions (left panel), both hemichannels are predicted to exhibit the same reductions in conductance, which results in a decrease in gj reflecting the combined closed probabilities of the hemichannels. Conversely, with 10 mM Mg2+ only on one side, the reduction in gj essentially reflects closure of the hemichannel on the high Mg2+ side, but resulted in a similar overall change in gj as simulated with symmetric 5 mM Mg2+.We performed computational simulations using parameters derived from the best fit model to assess how the component hemichannels in a GJ channel may be behaving in response to changes in cytoplasmic levels of Mgcal data , the cha2+ conditions over a longer time frame of 30 min. The open and closed probabilities plotted are those in which both hemichannels are open  during the application of Vj ramp. The black solid lines . The simulations reproduced the experimentally-observed behaviors of H18K and A13K GJ channels quite well. According to our model fitting, the impeded recovery in H18K channels is explained by increased entrance into the Mg2+-stabilized closed state promoted by the Vj- induced closure. The rapid and complete recovery of gj for A13K channels can be explained by a lack of Mg2+ binding and thus lack of a Mg2+-stabilized closed state reflected by an almost 450-fold decrease in the ratio k3/k4 compared to WT and the other variants. In addition, the estimated ratios k1/k2, which, according to our model, should reflect the intrinsic Vj sensitivities of the channels, corresponded well with electrophysiological data. For example, our model predicts that A13K channels should exhibit a comparable Vj sensitivity to WT Cx36 channels, while H18K channels should be significantly more Vj sensitive. The averaged gj-Vj curves obtained from voltage ramp experiments recorded at symmetric 5 mM Mg2+ conditions  between the shifts in the electrostatic potentials and the respective ratios of the permeability coefficients. The deviations of the data points from the predicted linear relationship between these two variables  and the D47 residue in particular, as playing a critical role, the NT domain was subsequently implicated because of an interdependence between Mg2+ and pH modulation that was affected by mutations in the NT domain , which is followed by a Mg2+ independent transition to a second closed state (the second step). However, the estimated values of the parameters for Model 4 predict large differences between WT and some of the variants, such as E3Q . We acknowledge that whether Mg2+ binds to the closed and not to the open conformation of Cx36 GJ channels lacks direct experimental evidence. However, our model fitting together with data on Cx46 regulation by Ca2+ strengthens this possibility and may represent a common mechanism by which Cxs are regulated by divalent cations.Thus, Model 5 provides the best overall fit to the data on V2+ on unitary conductance. Intracellular Mg2+ was shown to affect unitary conductance of Cx50 GJ channels  simulations of Cx26 hemichannels based on the Cx26 structure suggested that the reorganization of the electrostatic and van der Waals networks that is proposed to represent gating results in a substantial narrowing of the pore in the vicinity of the parahelical region in E1, which in Cx26 spans from E42 to F51 (2+ binding site potentially allowing for such a state-dependent binding event. These MD simulations also suggested that the reorganization of the network could change the angle of the bend in the TM1/E1 region thereby changing the conformation at the cytoplasmic entrance, which could represent an aspect of the coupling between NT and E1 regions and thus the ability of NT and E1 mutations to affect Mg2+ sensitivity.Mg2 to F51 . Assumin2+ modulation of Cx36 suggested that there may be a “deep-closed” conformation, in which both hemichannels of a Cx36 GJ channel are in a closed conformation resulting in the trapping of Mg2+ ions inside a pocket between two physical gates (k4 transition rates when both hemichannels reached a Mg2+-bound closed state, the probability to reach a double-closed state exceeded 90% during 2-3 hours of simulation, even at physiological concentrations of Mg2+. Given that the half-life of a Cx36 GJ channel can be short and on the order of 3 hrs (2+-induced stabilization of the closed conformation, they may exhibit a higher fraction of functional channels, a possibility consistent with a significantly higher value for gj,init.Previous studies of Mgal gates . Such a of 3 hrs , such a of 3 hrs . Given t2+ modulation could be mediated by titration of NT residues that plausibly cause allosteric changes that affect the binding of Mg2+, putatively ascribed to the E1 domain, and/or by alterations in gating that affect the state-dependent nature of the binding of Mg2+ suggested by our modeling. Absent mutations, posttranslational modifications, such as acetylation of the N-terminal methionine that was suggested as mechanism for imparting differential permeability characteristics to Cx26 (2+ influx and to lead to increases the strength of electrical coupling (2+ sensitivity by phosphorylation by CaMKII or other post-translational modifications can also occur, thereby potentially linking Mg2+ modulation to neuronal activity and the metabolic status of the brain.To our knowledge, there are no Cx36 mutations associated with disease that have been identified. However, mutations in 10 different human Cxs have been linked to over two dozen distinct genetic disorders with eight of them linked to mutations in Cx26 alone . Of part to Cx26 , may alscoupling . SubsequThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.TK, LK, LG, MS, NP-P, and VV designed the research and analyzed the data. TK, LG, and LK performed the experiments. TK performed the homology modeling and molecular dynamic simulations. MS constructed and applied mathematical models. LG, LK, and NP-P critically revised the manuscript. TK, MS, and VV wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Because the data volume of news videos is increasing exponentially, a way to quickly browse a sketch of the video is important in various applications, such as news media, archives and publicity. This paper proposes a news video summarization method based on SURF features and an improved clustering algorithm, to overcome the defects in existing algorithms that fail to account for changes in shot complexity. Firstly, we extracted SURF features from the video sequences and matched the features between adjacent frames, and then detected the abrupt and gradual boundaries of the shot by calculating similarity scores between adjacent frames with the help of double thresholds. Secondly, we used an improved clustering algorithm to cluster the color histogram of the video frames within the shot, which merged the smaller clusters and then selected the frame closest to the cluster center as the key frame. The experimental results on both the public and self-built datasets show the superiority of our method over the alternatives in terms of accuracy and speed. Additionally, the extracted key frames demonstrate low redundancy and can credibly represent a sketch of news videos. News video summarization tasks aim to extract the key frame sequence from a complete and long news video to summarize the news video, to meet the needs of users for quickly browsing and understanding the content . In videIn shot segmentation, the mainstream methods extract the features of video frames and judge whether they are located on the boundary of the shots by comparing the differences between the two frames. Wu et al.  introducIn the research of key frame extraction, Zhong et al.  realizedFor video summarization, many types of research have been carried out in the past and are ongoing until now. Souček et al.  presenteIn order to overcome the shortcomings of the above algorithms, this paper proposes a shot boundary detection algorithm based on SURF features  and a keIn brief, the major contributions of this paper are highlighted as follows.(1) In order to facilitate SURF based shot detection, we propose an inter-frame similarity metric based on the SURF feature points respectively extracted from two frames and the matched SURF feature points as well. Furthermore, in order to improve the detection accuracy of abrupt and gradual shots, we propose a double-threshold method to locate the shot boundary in the similarity curve.(2) We propose an improved HSV color histogram clustering algorithm to extract the key frame within a shot. The algorithm can dynamically determine the number of cluster centers without pre-setting in advance, so it can adapt to variations in the complexity of shots.The remainder of this paper is organized as follows. In The speeded up robust feature (SURF)  local fe(1)Feature point detectionThe SURF algorithm detects feature points by calculating the determinant of the Hessian matrix of images at different scales. A Hessian matrix is a second order partial derivative matrix. For the point The SURF algorithm uses box filters to approximate the Gaussian second partial derivatives. The Hessian matrix constructed by the SURF algorithm and the determinant of the Hessian matrix  are defi(2)Determination of the main directionIn order to make SURF feature points have rotation invariance, SURF determines a principal direction for each feature point. In the SURF algorithm, a circular region with feature points as the center and 6 s  as the radius is first established. Then, a 4 s-sized Haar wavelet template is used to scan the horizontal and vertical directions of the region, and the horizontal and vertical Haar wavelet responses are calculated. Finally, it scans the 60-degree sector window in this area to select the direction of the maximum total horizontal and vertical responses of Haar as the main direction of the feature point.(3)Generation of feature descriptorsA square box with side length of 20 s and direction as the main direction of the feature point is constructed at the feature point. In addition, the window is divided into 4 × 4 sub-boxes, where each box has a side length of 5 s. In each box, statistics of the horizontal Haar wavelet response, The video summarization method proposed in this paper can be divided into two parts. First, the whole news video is divided into individual video clips using the shot boundary detection based on SURF features. Then, the improved clustering algorithm is used to realize the key frame extraction in each shot. Finally, a summary of the news video is generated by arranging key frames chronologically. Note that, in this paper, key frames refer to the representative frames that can reflect the outline of the video contents.The SURF based shot boundary detection algorithm includes four steps: video preprocessing, SURF feature point extraction and matching, inter-frame similarity calculation, double thresholds selection and shot boundary detection.Because the main sources of news videos are TV programs, and news videos from TV usually have the platform logo and a scrolling subtitle at the bottom, the logo and the scrolling subtitle usually do not change when the video shot changes. Therefore, the presence of the platform logo and scrolling subtitles will affect the detection of the shot boundary. In order to improve the accuracy of detection, it is necessary to remove the logo and the scrolling subtitle in the news video frame first.M, the following equation is satisfied:x and y represent the coordinates of the pixel. The three-dimensional vector  indicates the three color channels of R, G, and B, respectively.The logo usually stays the same in the video, so we find the logo by looking for pixels that do not change or change very little in the video. For the removal of the logo, we first evenly select a number of frames M to perform the Hadamard product, so that we can get the image with the logo removed byAfter that, we use the original frames and the map After video preprocessing, we need to extract the features of video frames. Since SURF features keep stable for shot rotation, scale transformation, occlusion, lighting, and noise, this paper adopts the SURF algorithm to extract features of video frames.After extracting the feature points and descriptors of each frame, in order to measure the difference between frames, feature point matching between adjacent frames is used. Two feature matchers are provided in OpenCV, namely, the Brute-Force feature matcher and the FLANN feature matcher. In order to improve the processing efficiency of the algorithm, the FLANN matcher with its faster matching speed is used in this paper, where the K-D tree nearest neighbor algorithm is used to select match points. After the feature preliminary matching by FLANN, we further adopt the RANSAC algorithm to eliminate the feature mismatch. i and frame j is computed byi and j, i and j. i and frame j, respectively. Then, the similarity between all the adjacent frames is calculated and the similarity curve is depicted. After finishing the matching of SURF feature points of adjacent frames, we quantitatively measure inter-frame similarity to reflect the difference between the two frames. Since the matching result of the SURF features reflects the consistency of the two frames to a certain extent , we can exploit the SURF feature matching results to measure the similarity between video frames. Following this idea, the similarity score between frame Therefore, according to the characteristics of the mutation and gradient of the shots, this paper describes a double-threshold method for the detection of the shot boundary. For the set two thresholds (1) When the similarity of a frame is less than (2) A shot frame starts from a mutation or a gradient ending frame, and ends at the next mutation or gradient ending frame.After splitting the video into individual shots, the key frames need to be extracted further within each shot. We can select a representative frame from within the shot as the key frame using a clustering algorithm. The clustering based key frame extraction in shots mainly consists of four steps, calculation of an HSV color histogram, histogram clustering, merging small clusters and selection of the key frame.Compared with other color spaces, HSV color space is more consistent with human visual perception. Therefore, HSV color features are used as the feature representation for key frame extraction.After extracting HSV color histogram of each frame, we cluster histograms with the following clustering algorithm.Step 1k-th value in the newly generated HSV color normalized histogram and k-th value in the original HSV color histogram.For all frames in the video, in order to make the result unaffected by the number of pixels in the video frame, the color histogram is normalized byStep 2The algorithm generates a new cluster that contains the first frame of the video, the center of which is the value of the HSV color normalized histogram of the first frame.Step 3i and histogram j is defined asm represents the color component, namely hue (H), saturation (S) and value (V). k-th value of normalized histogram i in the m color component. N refers to the total series of histogram i. m in calculating distance.For frames that are beyond the first frame of the video, it calculates the distance between the HSV color histogram of that frame and the histogram of all the existing clusters. The distance between histogram Step 4C, if the distance between the frame and the cluster C, is less than the preset threshold, it is judged that the frame belongs to cluster C, and the center of cluster C is then updated. The cluster center is calculated byk-th value of the centroid histogram after updating, k-th value of the centroid histogram before updating, and k-th value of the color histogram of the newly added frames. S is the total number of frames contained in the cluster after adding the frame to cluster C.Given a cluster, C, the frame belongs to a new cluster, and the center of the cluster is initialized to the HSV color normalized histogram value of the frame.If there is no cluster Step 5It repeats steps 3–4 until all frames in the shot are processed.After the clustering is completed, a number of clusters are dynamically generated, with each cluster containing multiple video frames of the shots. However, the number of video frames in each cluster is inconsistent. Some clusters have more video frames, while some clusters have fewer frames. If the number of video frames contained in a cluster is small, it indicates that the cluster appears in the shot for a short time and is not representative. If key frames are extracted directly from a small cluster without additional processing, the extracted key frames will not be representative. In order to avoid redundancy of key frames and to make the selected key frames representative, it is necessary to merge the smaller clusters into the larger ones. We employ the following strategy to merge small clusters.S is the number of frames contained in the cluster, W is the weight, and F is the total number of frames of the shot.(1) If the size of a cluster satisfies(2) The histogram distance between the centers of this cluster and other clusters is calculated, and the cluster with the smallest distance is selected to perform the merge, and the center of the merged cluster is updated.k clusters, k key frames can be given. The key frames are arranged in chronological order of the video to generate the final video summarization.Based on the above method, the distance between the normalized histogram of the HSV frame and the center of the cluster is calculated by Equation (10). Then, the frame with the smallest distance from the center is selected as the key frame of the cluster. Therefore, if there are In order to confirm the validity of the method, a self-built news video dataset and a public dataset TVSum50  were useRecall and precision were used for evaluation metrics. Recall represents the ratio of the number of true positive shots to the total number of shots. Precision refers to the rate of the true shots that are detected.In order to verify the processing efficiency of the algorithm, we calculated the running speed of video summarization in frames per second. The results are shown in It can be seen from the results that the speed of our algorithm in SURF feature point extraction is 41.27 frames per second, which is 29.6% faster than Li S.  and 56.1In order to verify the effect of this algorithm on shot boundary detection, we tested the recall and precision under different types of videos, in comparison with the algorithms by Feng , Wang 3, TransNeFrom Threshold will also affect the extraction of key frames. The higher the threshold is set, the easier the video frames will be assigned into an existing cluster, and the lower the similarity of frames within the cluster will be. On the contrary, the lower the threshold is set, the more difficult it is for a frame to be merged into an existing cluster, and the easier it is to generate a new cluster, which may cause redundancy of key frame extraction.In order to qualitatively examine the performance of our method, we conduct a comparison with a video summary made manually by users. Due to the limited space, We then evaluated the video summarization results extracted by the algorithm, taking segment 2 of “News 30” as a concrete example. As shown in Effects of different local descriptors. In order to investigate the effectiveness of different local descriptors on the results, we tested the feature extraction speed and quality of different local descriptors. The visual and numeric comparisons on news video datasets are shown in Effects of different color spaces. We compared the intra-class distances of the clips of “News in 30 Minutes” in different color spaces. In order to ensure fairness, we normalized the histograms before calculation, and we use Bhattacharyya distance to represent the histogram distance. As shown in News video summarization is a process of rapid summarization of news videos. This paper proposes a method of news video summarization based on SURF features and a clustering algorithm. The method consists of two steps: shot segmentation and key frame extraction. First, we preprocess the news video according to its characteristics, extract the SURF feature of frames as local features, and match the SURF feature points between consecutive frames. Then, we calculate the similarity scores between adjacent frames based on the matched points and determine the shot boundaries with the proposed double-threshold method. After the segmentation of the shots, color histograms of each frame in the shot are dynamically clustered, where smaller clusters are merged, and the key frames are extracted from each cluster to create the summary of the news video. The experimental results on news video datasets show that our method achieves an average accuracy of 93.33% and a recall rate of 97.22% in shot boundary detection. Moreover, the extracted key frames show a low redundancy and are highly similar to the video summaries made manually by users.


The COVID-19 pandemic severely affected many aspects of human life. While most health agencies agree mask wearing and physical distancing reduce viral transmission, efforts to improve the assessment of these behaviors are lacking. This study aimed to develop a direct observation video method [Viral Transmission (VT)-Scan] for assessing COVID-19 transmission behaviors and related factors . A wearable video device (WVD) was used to obtain videos of outdoor, public areas. The videos were examined to extract relevant information. All outcomes displayed good to excellent intra- and inter-reliability with intra-class correlation coefficients ranging from 0.836 to 0.997. The majority of people had a mask (60.8%) but 22.1% of them wore it improperly, 45.4% were not physical distancing, and 27.6% were simultaneously mask and physical distancing non-compliant. Transmission behaviors varied by demographics with white, obese males least likely to be mask-compliant and white, obese females least likely to physical distance. Certain environments  were identified as “hot spots” where higher rates of adverse transmission behaviors occurred. This study introduces a reliable method for obtaining objective data on COVID-19 transmission behaviors and related factors which may be useful for agent-based modeling and policy formation. The SARS-CoV-2 virus that causes coronavirus disease 2019 (COVID-19), became a global public health crisis in early 2020. Since then, the pandemic has grown considerably not only in reach but in magnitude . In just over a year and a half, there have been nearly 212 million cases globally with over 4.4 million virus-related deaths. In the U.S. alone, 37.4 million cases have been confirmed with 622,459 deaths as of 23 August 2021 . Out of the total numbers of individuals described, 111 individuals were described by reviewer 1 during review sessions 1 and 2 and 117 were described by both reviewer 1 and 2 during review session 1. All told, about 10% of the people described during each review session could not be timestamp matched to a person described during another corresponding review session.Counts and ICCs for the five min epochs are provided in κ statistics were calculated to assess intra- and inter-rater agreement on the presence/absence of characteristics. As can be seen in κ statistics were significant (p < 0.001) and according to the interpretation criteria, 60% of the intra-reviewer κ statistics indicated strong agreement while 40% indicated moderate agreement. For inter-reviewer, 40% of κ statistics were strong and 60% moderate. Intra-reviewer percent agreement averages were 92.8% and 91.8% for the five COVID-19 behaviors and personal characteristics, respectively. Average percent agreement for inter-reviewer COVID-19 behaviors was 93.6% and 89.1% for personal characteristics.Based on descriptions of the same people, n = 111) and inter-reviewer  coordinate differences did not differ significantly (p= 0.51). Intra- and inter-reliability coordinate differences did not differ significantly between people not moving vs. moving , physical distancing vs. not physical distancing , and at a node vs. not at a node   .n = 87), which was the week immediately prior to the start of the semester. With the onset of the semester, the numbers described increased substantially and remained fairly consistent throughout the remaining weeks of data collection . A total of 916 people were described during the case study in 372 min of video which equates to 2.46 people/min. The fewest people were seen during the first week of data collection  = 32.1; p < 0.001)] and those not moving compared to those moving . Non-compliance with physical distancing recommendations also varied by race and activity as well as sex/gender and weight status. Less compliance was seen in whites , females , obese , and individuals not moving . Similar differences were noted (except for weight status) for simultaneous non-compliance with mask and physical distance recommendations for race , gender , and activity . The only significant difference in touching behavior was between individuals not moving and those moving. Those observed not moving were much more likely to touch a surface than those moving .A breakdown of COVID-19 transmission behaviors by personal characteristics are provide in Χ2  = 14.8; p = 0.01; adjusted standardized residual week 6 = 3.8]  = 19.0; p = 0.002]  = 17.4; p = 0.002]. Mask and physical distancing compliance were high at baseline, dropped around 20% from weeks 2 through 4, then showed a rebound to near baseline levels by week 6. Descriptions of COVID-19 transmission behaviors by place are given in Χ2  = 17.8; p = 0.003]. Non-compliance with both mask wearing and physical distancing was highest at non-node sites, crosswalks and non-academic campus building exits/entrances . Surface touching was highest at non-academic campus building exits/entrances and academic building exits/entrances . A visual representation of the spatial distribution of mask and physical distancing non-compliance is provided in In 6 = 3.8] . The per= 0.002] . At baseThe purpose of this study was to create a direct observation video method (VT-scan) for assessing outcomes related to COVID-19 transmission behaviors and associated personal and environmental factors. Findings indicate that video obtained using VT-scan procedures contains information on these outcomes that can be extracted in a consistent manner. Both intra- and inter-reliability were acceptable across a heterogeneous set of behaviors, personal characteristics, and environmental settings. The utility of VT-scan was evidenced in a pilot study where we demonstrated that the distribution of COVID-19 transmission behaviors varied across personal and environmental factors as well as time. Direct observation, with and without the use of video, has been used extensively to study human behaviors in public open spaces  ,39,40. AA common finding across studies and observation approaches is that they tend to be highly reliable or consistent in discerning characteristics of interest across observers and time. The same was found in this study for extracting data on multiple COVID-19 related factors from videos of people going about their everyday business in outdoor areas. When using direct observation combined with video capture, reliability is essentially a product of data collection and data extraction. In the current study, data collection was optimized by utilizing one trained observer, a pre-determined route, and a high-resolution WVD. Thus, the videos (data) were obtained using a standardized procedure that enhanced the video review process. The effect on reliability of using alternative data collection procedures  definitely warrants examination. However, it is recommended to at least pre-plan a route and use a high-resolution video device. Data extraction is dependent on the quality of the video (set during data collection), the decision rules for labeling characteristics of interest, and how well reviewers follow the rules (set by training). Our aim was to create decision rules that better aligned VT-scan with the “objective” nature of direct observation. A set of criteria were established for the outcomes to lessen the reliance of the reviewers on their own “subjective” criteria which is an advancement over other observation approaches used to study human behavior ,42. The The case study further demonstrated the value of VT-scan. Characteristics were assigned to most people seen in the videos along with when and where they were when observed. This is consistent with other studies that have repeatedly shown that the observation method is useful for providing information on humans such as age, sex/gender, race/ethnicity, and physical activity (type and intensity) as well as the behavior settings they are in ,42. In aThere are other added benefits to incorporating video into the direct observation method. First, data from video vs. in-person only assessments tend to be more accurate simply because computer software can be used to manipulate the videos  increasing the odds the outcome of interest is correctly identified ,22. VideThe VT-scan was useful for describing COVID-19-related factors as well as their interactions with each other. Further, the sensitivity of VT-scan was exemplified in the time fluctuations noted over the course of case study observations. During the case study, a State of Emergency was in effect that mandated mask wearing and physical distancing in indoor and outdoor areas with any failure to comply constituting a criminal offence. However, these rules were not enforced until week six of the case study and at that point, VT-scan noted a substantial rise in mask/physical distancing adherence. Of importance was the findings on the variation in COVID-19 transmission behaviors by personal and environmental factors. This is consistent with previous research on variations in infections and transmission behaviors across study sample characteristics . HoweverWhile the case study was done to demonstrate the utility of VT-scan, the links observed between demographic characteristics and transmission behaviors warrant discussion. First, individuals not moving (sitting or standing) were less compliant with masks, physical distancing, and surface touching than those moving. Most of the non-movers were sitting at tables where table dimensions did not permit physical distancing. In addition, people at tables could remove masks according to University and State mandates under the guise that they were drinking or eating (even though tables were in public areas and not directly associated with a restaurant). Whites compared with non-whites and females compared with males were less compliant with masks while not physical distancing whereas obese individuals were less likely to practice physical distancing. These findings agree with previous studies using self-report methodology and may reflect a myriad of reasons including perceived risk , social and economic factors , and personal beliefs/attitudes  ,47,48,49The findings of this study should be considered in the context of its limitations. Data were collected during two months in the late summer early fall on and near a mid-sized University campus perhaps restricting generalizations to dissimilar seasons/areas. However, the influence of this limitation may be somewhat diminished given sections around campus represented typical residential and business zones. Nevertheless, unforeseen issues may arise when extending VT-scan to different areas or even different situations. For example, the accuracy of direct observation has been shown to fall off slightly when larger groups of people are observed (as might be found during rush hour in New York City) . RelatedThis study provides evidence on the reliability of a new video-enhanced, direct observation method (VT-scan) for systematically assessing COVID-19 transmission behaviors and related factors. It demonstrates that data of this nature can be successfully captured in and extracted from video. Although VT-scan’s utility for wide-spread, surveillance efforts may be limited and only reach fruition when integrated with AI techniques, the method is highly replicable and has immediate value for informing localized, agent-based models and local policy aimed at mitigating the COVID-19 pandemic as well as other and future epidemics and pandemics due to airborne pathogens.