Many telepresence robots are equipped with a forward-facing camera for video communication and a downward-facing camera for navigation. In this paper, we propose to stitch videos from the FF-camera with a wide-angle lens and the DF-camera with a fisheye lens for telepresence robots. We aim at providing more compact and efficient visual feedback for the user interface of telepresence robots with user-friendly interactive experiences. To this end, we present a multi-homography-based video stitching method which stitches videos from a wide-angle camera and a fisheye camera. The method consists of video image alignment, seam cutting, and image blending. We directly align the wide-angle video image and the fisheye video image based on the multi-homography alignment without calibration, distortion correction, and unwarping procedures. Thus, we can obtain a stitched video with shape preservation in the non-overlapping regions and alignment in the overlapping area for telepresence. To alleviate ghosting effects caused by moving objects and or moving cameras during telepresence robot driving, an optimal seam is found for aligned video composition, and the optimal seam will be updated in subsequent frames, considering spatial and temporal coherence. The final stitched video is created by image blending based on the optimal seam. We conducted a user study to demonstrate the effectiveness of our method and the superiority of telepresence robots with a stitched video as visual feedback.
Besides, some work was designed for real-time processing or time-critical applications. For video surveillance applications, He and Yu @cite_13 employed a background modeling algorithm and a change-detection-based optimal seam selection approach to stitch videos captured by fixed cameras. A Multi-UAV-based video surveillance system, SkyStitch @cite_27 , was designed and implemented for real-time aerial surveillance, employing flight information (e.g., the UAV attitude and GPS location) got from the flight controller as assistance. @cite_31 introduced a real-time video stitching method by implementing and improving a feature-based algorithm on a field-programable gate array (FPGA). Apart from hardware acceleration, EI- @cite_16 developed a real-time method to stitch independent videos streamed by different mobile phones, while @cite_43 stitched several live videos into a @math field of view and spread the stitched video based on GPU.
@cite_43 @cite_27 @cite_31 @cite_16 @cite_13
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Recent advances in unmanned aerial vehicle (UAV) technologies have made it possible to deploy an aerial video surveillance system to provide an unprecedented aerial perspective for ground monitoring in real time. Multiple UAVs would be required to cover a large target area, and it is difficult for users to visualize the overall situation if they were to receive multiple disjoint video streams. To address this problem, we designed and implemented SkyStitch, a multiple-UAV video surveillance system that provides a single and panoramic video stream to its users by stitching together multiple aerial video streams. SkyStitch addresses two key design challenges: (i) the high computational cost of stitching and (ii) the difficulty of ensuring good stitching quality under dynamic conditions. To improve the speed and quality of video stitching, we incorporate several practical techniques like distributed feature extraction to reduce workload at the ground station, the use of hints from the flight controller to improve stitching efficiency and a Kalman filter-based state estimation model to mitigate jerkiness. Our results show that SkyStitch can achieve a stitching rate that is 4 times faster than existing state-of-the-art methods and also improve perceptual stitching quality. We also show that SkyStitch can be easily implemented using commercial off-the-shelf hardware. This paper presents a parallax-robust video stitching technique for timely synchronized surveillance video. An efficient two-stage video stitching procedure is proposed in this paper to build wide Field-of-View (FOV) videos for surveillance applications. In the stitching model calculation stage, we develop a layered warping algorithm to align the background scenes, which is location-dependent and turned out to be more robust to parallax than the traditional global projective warping methods. On the selective seam updating stage, we propose a change-detection based optimal seam selection approach to avert ghosting and artifacts caused by moving foregrounds. Experimental results demonstrate that our procedure can efficiently stitch multi-view videos into a wide FOV video output without ghosting and noticeable seams. We conducted high-frame-rate (HFR) video mosaicing for real-time synthesis of a panoramic image by implementing an improved feature-based video mosaicing algorithm on a field-programmable gate array (FPGA)-based high-speed vision platform. In the implementation of the mosaicing algorithm, feature point extraction was accelerated by implementing a parallel processing circuit module for Harris corner detection in the FPGA on the high-speed vision platform. Feature point correspondence matching can be executed for hundreds of selected feature points in the current frame by searching those in the previous frame in their neighbor ranges, assuming that frame-to-frame image displacement becomes considerably smaller in HFR vision. The system we developed can mosaic 512×512 images at 500 fps as a single synthesized image in real time by stitching the images based on their estimated frame-to-frame changes in displacement and orientation. The results of an experiment conducted, in which an outdoor scene was captured using a hand-held camera-head that was quickly moved by hand, verify the performance of our system. In this paper, we extend image stitching to video stitching for videos that are captured for the same scene simultaneously by multiple moving cameras. In practice, videos captured under this circumstance often appear shaky. Directly applying image stitching methods for shaking videos often suffers from strong spatial and temporal artifacts. To solve this problem, we propose a unified framework in which video stitching and stabilization are performed jointly. Specifically, our system takes several overlapping videos as inputs. We estimate both inter motions (between different videos) and intra motions (between neighboring frames within a video). Then, we solve an optimal virtual 2D camera path from all original paths. An enlarged field of view along the virtual path is finally obtained by a space-temporal optimization that takes both inter and intra motions into consideration. Two important components of this optimization are that: 1) a grid-based tracking method is designed for an improved robustness, which produces features that are distributed evenly within and across multiple views and 2) a mesh-based motion model is adopted for the handling of the scene parallax. Some experimental results are provided to demonstrate the effectiveness of our approach on various consumer-level videos and a Plugin, named “Video Stitcher” is developed at Adobe After Effects CC2015 to show the processed videos. We describe an algorithm for generating panoramic video from unstructured camera arrays. Artifact-free panorama stitching is impeded by parallax between input views. Common strategies such as multi-level blending or minimum energy seams produce seamless results on quasi-static input. However, on video input these approaches introduce noticeable visual artifacts due to lack of global temporal and spatial coherence. In this paper we extend the basic concept of local warping for parallax removal. Firstly, we introduce an error measure with increased sensitivity to stitching artifacts in regions with pronounced structure. Using this measure, our method efficiently finds an optimal ordering of pair-wise warps for robust stitching with minimal parallax artifacts. Weighted extrapolation of warps in non-overlap regions ensures temporal stability, while at the same time avoiding visual discontinuities around transitions between views. Remaining global deformation introduced by the warps is spread over the entire panorama domain using constrained relaxation, while staying as close as possible to the original input views. In combination, these contributions form the first system for spatiotemporally stable panoramic video stitching from unstructured camera array input. In computer vision, video stitching is a very challenging problem. In this paper, we proposed an efficient and effective wide-view video stitching method based on fast structure deformation that is capable of simultaneously achieving quality stitching and computational efficiency. For a group of synchronized frames, firstly, an effective double-seam selection scheme is designed to search two distinct but structurally corresponding seams in the two original images. The seam location of the previous frame is further considered to preserve the interframe consistency. Secondly, along the double seams, 1-D feature detection and matching is performed to capture the structural relationship between the two adjacent views. Thirdly, after feature matching, we propose an efficient algorithm to linearly propagate the deformation vectors to eliminate structure misalignment. At last, image intensity misalignment is corrected by rapid gradient fusion based on the successive over relaxation iteration (SORI) solver. A principled solution to the initialization of the SORI significantly reduced the number of iterations required. We have compared favorably our method with seven state-of-the-art image and video stitching algorithms as well as traditional ones. Experimental results show that our method outperforms the existing ones compared in terms of overall stitching quality and computational efficiency. Images videos captured by portable devices (e.g., cellphones, DV cameras) often have limited fields of view. Image stitching, also referred to as mosaics or panorama, can produce a wide angle image by compositing several photographs together. Although various methods have been developed for image stitching in recent years, few works address the video stitching problem. In this paper, we present the first system to stitch videos captured by hand-held cameras. We first recover the 3D camera paths and a sparse set of 3D scene points using CoSLAM system, and densely reconstruct the 3D scene in the overlapping regions. Then, we generate a smooth virtual camera path, which stays in the middle of the original paths. Finally, the stitched video is synthesized along the virtual path as if it was taken from this new trajectory. The warping required for the stitching is obtained by optimizing over both temporal stability and alignment quality, while leveraging on 3D information at our disposal. The experiments show that our method can produce high quality stitching results for various challenging scenarios. Stitching videos captured by handheld devices is very useful, but also very challenging due to the heavy and independent shakiness in the videos. In this paper, we propose a hand-taken video stitching method which combines the techniques of video stitching and stabilization together into a unified optimization framework. In this way, our method can compute the most optimal stabilization and stitching results with respect to each other, which outperforms previous methods that take stabilization and stitching as separate operations. Our method is based on the framework of bundled camera paths [ 2013]. We present a novel unified camera paths optimization formulation which consists of two stabilization terms and one stitching term. We also present a corresponding iterative solver that finds best stitching and stabilization solutions numerically. We compare our method with previous methods, and the experiments demonstrate the effectiveness of our method. In this paper, a novel content-preserving video stitching method is proposed. Video stitching has to deal with moving objects in the overlapped area (OA) which often cause problems of structural misalignment or ghost effect. To this end, the proposed method first finds an optimal seam that does not pass through the moving objects, by using the extended dynamic programing (DP) technique based on the energy minimization. Then the content-aware adaptive blending is performed which effectively reduces the color discontinuity while restricting the ghost effect caused by moving objects in the OA. In addition, to reduce the computational complexity, a partial seam-update (PSU) scheme is proposed, in which the seam is re-calculated only for a part of the seam passing through the moving object. Experimental results demonstrate that the proposed method is superior to the conventional ones in terms of both subjective quality and computational complexity. In addition, this method achieves real-time performance on mobile platform, making it applicable to consumer electronic devices. In Image Processing, a very trivial task is to detect the changes in the multiple images of the same scene of a real time instant. The task is not only trivial but also very indispensable as it brings into play a great number of diversified subject area applications such as, remote sensing, surveillance, medical diagnosis and treatment, security surveillance, and underwater sensing. The main perspective of this survey is to give a nomenclature study of the general processing steps and prime decision rules used in the advanced change detection algorithms, which are employed for the real time video surveillance. The real time video surveillance models encompass the predictive and the background modelling techniques. The survey also emphasizes on the comparison of the processing speed of the various change detection algorithms applied in real time video surveillance. We present a system for automatically extracting the region of interest (ROI) and controlling virtual cameras' control based on panoramic video. It targets applications such as classroom lectures and video conferencing. For capturing panoramic video, we use the FlyCam system that produces high resolution, wide-angle video by stitching video images from multiple stationary cameras. To generate conventional video, a region of interest can be cropped from the panoramic video. We propose methods for ROI detection, tracking, and virtual camera control that work in both the uncompressed and compressed domains. The ROI is located from motion and color information in the uncompressed domain and macroblock information in the compressed domain, and tracked using a Kalman filter. This results in virtual camera control that simulates human controlled video recording. The system has no physical camera motion and the virtual camera parameters are readily available for video indexing. User generated videos with mobile phone cameras are becoming more and more ubiquitous allowing people to share live content with remote parties, possibly in real-time. However with limited mobile phone capabilities, these videos are usually of small resolution, resulting in a small field of view for acceptable quality. Fortunately with the proliferation of video capture-enabled mobile phones, there is high chance that one or more persons will be shooting the same scene from different views. In this demonstration, we are showing an end-to-end system which receives video streams coming from different mobile phones, time synchronizes the streams and produces a single composite mosaic video, and all of this is done in real-time. The proposed system operates without coordination between users. The system has been tested under various capturing conditions such as indoor, outdoor, day and night conditions. Explosive growth of surveillance video data presents formidable challenges to its browsing, retrieval and storage. Video synopsis, an innovation proposed by Peleg and his colleagues, is aimed for fast browsing by shortening the video into a synopsis while keeping activities in video captured by a camera. However, the current techniques are offline methods requiring that all the video data be ready for the processing, and are expensive in time and space. In this paper, we propose an online and efficient solution, and its supporting algorithms to overcome the problems. The method adopts an online content-aware approach in a step-wise manner, hence applicable to endless video, with less computational cost. Moreover, we propose a novel tracking method, called sticky tracking, to achieve high-quality visualization. The system can achieve a faster-than-real-time speed with a multi-core CPU implementation. The advantages are demonstrated by extensive experiments with a wide variety of videos. The proposed solution and algorithms could be integrated with surveillance cameras, and impact the way that surveillance videos are recorded. The possibility of sharing one's point of view makes use of wearable cameras compelling. These videos are often long, boring and coupled with extreme shake as the camera is worn on a moving person. Fast forwarding (i.e. frame sampling) is a natural choice for faster video browsing. However, this accentuates the shake caused by natural head motion in an egocentric video, making the fast forwarded video useless. We propose EgoSampling, an adaptive frame sampling that gives more stable, fast forwarded, hyperlapse videos. Adaptive frame sampling is formulated as energy minimization, whose optimal solution can be found in polynomial time. We further turn the camera shake from a drawback into a feature, enabling the increase of the field-of-view. This is obtained when each output frame is mosaiced from several input frames. Stitching multiple frames also enables the generation of a single hyperlapse video from multiple egocentric videos, allowing even faster video consumption. This paper introduces Videograph, a new tool for video mining and visu alizing the structure of the plot of a video sequence. The main idea is to lstitchr together similar scenes which are apart in time. We give a fast algorithm to do stitching and we show case studies, where our approach (a) gives good features for classification (91 accuracy), and (b) results in Videographs which reveal the logical structure of the plot of the video clips. Live wide-area persistent surveillance (WAPS) systems must provide effective multi-target tracking on downlinked video streams in real-time. This paper presents the first published aerial tracking system that is documented to process over 100 megapixels per second. The implementation addresses the challenges with the mosaicked, low-resolution, grayscale NITF imagery provided by most currently fielded WAPS platforms and the flexible computation architecture required to provide real-time performance. This paper also provides ground-truth for repeatable evaluation of wide-area persistent surveillance on a 2009 dataset collected by AFRL [1] that is available to the public as well as a quantitative analysis of this real-time implementation. To our knowledge, this is the only publication that (1) provides details of a real-time implementation for detection and tracking in (2) mosaicked, composed imagery from a fielded WAPS sensor, and (3) provides annotation data and quantitative analysis for repeatable WAPS tracking experimentation in the computer vision community. In this paper we introduce a new algorithm for image and video texture synthesis. In our approach, patch regions from a sample image or video are transformed and copied to the output and then stitched together along optimal seams to generate a new (and typically larger) output. In contrast to other techniques, the size of the patch is not chosen a-priori, but instead a graph cut technique is used to determine the optimal patch region for any given offset between the input and output texture. Unlike dynamic programming, our graph cut technique for seam optimization is applicable in any dimension. We specifically explore it in 2D and 3D to perform video texture synthesis in addition to regular image synthesis. We present approximative offset search techniques that work well in conjunction with the presented patch size optimization. We show results for synthesizing regular, random, and natural images and videos. We also demonstrate how this method can be used to interactively merge different images to generate new scenes. The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU's to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames. We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras. It is based on a new structure from motion formulation for the 3D reconstruction of a single moving point with known motion dynamics. Our main contribution is a new bundle adjustment procedure, which in addition to optimizing the camera poses, regularizes the point trajectory using a prior based on motion dynamics (or specifically flight dynamics). Furthermore, we can infer the underlying control input sent to the UAVs autopilot that determined its flight trajectory. Our method requires neither perfect single-view tracking nor appearance matching across views. For robustness, we allow the tracker to generate multiple detections per frame in each video. The true detections and the data association across videos is estimated using robust multi-view triangulation and subsequently refined in our bundle adjustment formulation. Quantitative evaluation on simulated data and experiments on real videos from indoor and outdoor scenes shows that our technique is superior to existing methods. A new generation of active 3D range sensors, such as time-of-flight cameras, enables recording of full-frame depth maps at video frame rate. Unfortunately, the captured data are typically starkly contaminated by noise and the sensors feature only a rather limited image resolution. We therefore present a pipeline to enhance the quality and increase the spatial resolution of range data in real-time by upsampling the range information with the data from a high resolution video camera. Our algorithm is an adaptive multi-lateral upsampling filter that takes into account the inherent noisy nature of real-time depth data. Thus, we can greatly improve reconstruction quality, boost the resolution of the data to that of the video sensor, and prevent unwanted artifacts like texture copy into geometry. Our technique has been crafted to achieve improvement in depth map quality while maintaining high computational efficiency for a real-time application. By implementing our approach on the GPU, the creation of a real-time 3D camera with video camera resolution is feasible.