Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT HARDWARE <s> Gesture recognition is essential for human — machine interaction. In this paper we propose a method to recognize human gestures using a Kinect® depth camera. The camera views the subject in the front plane and generates a depth image of the subject in the plane towards the camera. This depth image is then used for background removal, followed by generation of the depth profile of the subject. In addition to this, the difference between subsequent frames gives the motion profile of the subject and is used for recognition of gestures. These allow the efficient use of depth camera to successfully recognize multiple human gestures. The result of a case study involving 8 gestures is shown. The system was trained using a multi class Support Vector Machine. <s> BIB001 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT HARDWARE <s> Recent advances in 3D depth cameras such as Microsoft Kinect sensors (www.xbox.com/en-US/kinect) have created many opportunities for multimedia computing. The Kinect sensor lets the computer directly sense the third dimension (depth) of the players and the environment. It also understands when users talk, knows who they are when they walk up to it, and can interpret their movements and translate them into a format that developers can use to build new experiences. While the Kinect sensor incorporates several advanced sensing hardware, this article focuses on the vision aspect of the Kinect sensor and its impact beyond the gaming industry. <s> BIB002 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT HARDWARE <s> Current research on skeleton tracking techniques focus on image processing in conjunction with a video camera constrained by bones and joint movement detection limits. The paper proposed 3D skeleton tracking technique using a depth camera known as a Kinect sensor with the ability to approximate human poses to be captured, reconstructed and displayed 3D skeleton in the virtual scene using OPENNI, NITE Primesense and CHAI3D open source libraries. The technique could perform the bone joint movement detections in real time with correct position tracking and display a 3D skeleton in a virtual environment with abilities to control 3D character movements for the future research. <s> BIB003 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT HARDWARE <s> This paper presents a novel fall detection system based on the Kinect sensor. The system runs in real-time and is capable of detecting walking falls accurately and robustly without taking into account any false positive activities (i.e. lying on the floor). Velocity and inactivity calculations are performed to decide whether a fall has occurred. The key novelty of our approach is measuring the velocity based on the contraction or expansion of the width, height and depth of the 3D bounding box. By explicitly using the 3D bounding box, our algorithm requires no pre-knowledge of the scene (i.e. floor), as the set of detected actions are adequate to complete the process of fall detection. <s> BIB004 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT HARDWARE <s> With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision algorithms and applications. The reviewed approaches are classified according to the type of vision problems that can be addressed or enhanced by means of the Kinect sensor. The covered topics include preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping. For each category of methods, we outline their main algorithmic contributions and summarize their advantages/differences compared to their RGB counterparts. Finally, we give an overview of the challenges in this field and future research trends. This paper is expected to serve as a tutorial and source of references for Kinect-based computer vision researchers. <s> BIB005 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT HARDWARE <s> Elderly suffers from injuries or disabilities through falls every year. With a high likelihood of falls causing serious injury or death, falling can be extremely dangerous, especially when the victim is home-alone and is unable to seek timely medical assistance. Our fall detection systems aims to solve this problem by automatically detecting falls and notify healthcare services or the victim’s caregivers so as to provide help. In this paper, development of a fall detection system based on Kinect sensor is introduced. Current fall detection algorithms were surveyed and we developed a novel posture recognition algorithm to improve the specificity of the system. Data obtained through trial testing with human subjects showed a 26.5% increase in fall detection compared to control algorithms. With our novel detection algorithm, the system conducted in a simulated ward scenario can achieve up to 90% fall detection rate. <s> BIB006 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT HARDWARE <s> This paper presents a new and efficient algorithm for complex human activity recognition using depth videos recorded from a single Microsoft Kinect camera. The algorithm has been implemented on videos recorded from Kinect camera in OpenNI video file format (.oni). OpenNI file format provides a combined video with both RGB and depth information. An OpenNI specific dataset of such videos has been created containing 200 videos of 8 different activities being performed by different individuals. This dataset should serve as a reference for future research involving OpenNI skeleton tracker. The algorithm is based on skeleton tracking using state of the art OpenNI skeleton tracker. Various joints and body parts in human skeleton have been tracked and the selection of these joints is made based on the nature of the activity being performed. The change in position of the selected joints and body parts during the activity has been used to construct feature vectors for each activity. Support vector machine (SVM) multi-class classifier has been used to classify and recognize the activities being performed. Experimental results show the algorithm is able to successfully classify the set of activities irrespective of the individual performing the activities and the position of the individual in front of the camera. <s> BIB007 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT HARDWARE <s> Computational modeling of human behavior has become a very important field of computer vision. Gesture recognition allows people to interact with machines in a natural way without the use of dedicated I/O devices. This paper presents a simple system that can recognize dynamic and static gestures using the depth map and the higher level output (skeleton and facial features) provided by a Kinect sensor. Two approaches are chosen for the recognition task: the Dynamic Time Warping Algorithm is used to recognize dynamic gestures, while a Bayesian classifier is used for the static gestures/postures. In contrast with some specialized methods presented in the literature, the current approach is very generic and can be used with minimal modification for recognizing a large variety of gestures. As a result, it can be deployed in a multitude of fields from security (monitoring rooms and sending alarm signals), medicine (helping people with physical disabilities) to education and so on. The tests results show that the system is accurate, easy to use and highly customizable. <s> BIB008 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT HARDWARE <s> Microsoft Kinect had a key role in the development of consumer depth sensors being the device that brought depth acquisition to the mass market. Despite the success of this sensor, with the introduction of the second generation, Microsoft has completely changed the technology behind the sensor from structured light to Time-Of-Flight. This paper presents a comparison of the data provided by the first and second generation Kinect in order to explain the achievements that have been obtained with the switch of technology. After an accurate analysis of the accuracy of the two sensors under different conditions, two sample applications, i.e., 3D reconstruction and people tracking, are presented and used to compare the performance of the two sensors. <s> BIB009 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT HARDWARE <s> Motivated by the reports about assaults on women, especially college girls, in China, we take the first step to explore possibility of using off-the-shelf smartphone for physical assault detection. The most difficult one among challenges in our design is the extraordinary complexity and diversity of various assault instances, which lead to an extremely hard, if not impossible, to perform fine-grained recognition. To this end, we decide to focus on the characteristics of intensity and irregularity, based on which several features are extracted. Moreover, we proposed a combinatorial classification scheme considering individuality of user’s ADLs(Activities of Daily Living) and universality of differences between ADLs and assaults to most people. The data we used for evaluation are collected from simulated assaults which are performed by our volunteers in controlled settings. Our experiment results showed that physical assaults could be distinguished with the majority of ADLs in our proposed feature space, and our proposed system could correctly detect most instances of aggravated assault with low false alarm rate and short delay. <s> BIB010 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT HARDWARE <s> A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 apartments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near (within 4 m) versus far fall locations, and occluded versus not occluded fallers. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved. <s> BIB011 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT HARDWARE <s> RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms. <s> BIB012 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> KINECT HARDWARE <s> Motivated by the reports about assaults on women, we take the first step to explore possibility of using off-the-shelf smartphone for physical assault detection. There are several kinds of crime offenses against persons, such as gunshot, battery, abuse, kidnapping and so on, which are distinguished by form, severity, duration, etc. In this paper, we aim at detecting those severe and non-instantaneous physical assaults using accelerometer in smartphone. We collected 100 surveillance videos involving aggravated assaults, and extract the the pattern of actions for an individual under assaulting. The most difficult one among challenges in our design is the extraordinary complexity and diversity of actions under assaulting, which lead to an extremely hard, if not impossible, to perform fine-grained recognition. To this end, we decide to focus on the intensity and irregularity characteristics of aggravated assaults, based on which several features from time domain and frequency domain are extracted. Moreover, we proposed a combinatorial classification scheme considering individuality of user’s ADLs (Activities of Daily Living) and universality of differences between ADLs and assaults to most people. The data we used for training and testing are collected from simulated aggravated assaults which are performed by our volunteers in controlled settings. Our experiment results showed that aggravated assaults could be distinguished with the majority of ADLs in our proposed feature space, and our proposed system could correctly detect most instances of aggravated assault (FNR = 11.75 %) with low false alarm rate (0.067 times per day) and short delay (6.89 s). <s> BIB013
The Kinect refer to the camera which has been provided by Microsoft in 2010 for the Xbox 360 console, and it is known as Kinect v1 (Figure 3 .a). In 2014, Microsoft developed the Kinect v2 ( Figure 3 .b) which improve some hardware and software features BIB008 . Kinect hardware contains RGB camera, IR camera, IR projector, multi-array microphone and a tilt moto BIB008 BIB012 BIB005  BIB009 : • RGB camera: provide colorful images with resolution 640×480 pixels for v1 at 30 Hz. And colorful pictures with resolution 1920×1080 pixels for v2. Both contain 30 frames\s. • IR camera and IR projector: together they provide the 3D depth sensor, which offers depth images with resolution 320×240 pixels for v1 and has a ranging limit of 0.8 ∼ 3.5 m and has the Field of View (FOV) 62º(h) x 48.6º(v). And for v2, it provides depth images with resolution 512×424 pixels and has ranging limit of 0.4 ∼ 4.5m. Both have the frame rate of 30 frames\s. The v2 increased FOV of 70º(h) x 60º(v). • Multi-array microphone: consist of four microphones to make live communication available. It can process up to 16-bit audio signals at 16 kHz sample rate. • Tilt motor: it is existing only on Kinect v1 to allow the sensor adjustment and can tilt the sensor up to 27º either up or down. The above hardware features make the Kinect able to provide full-body 3D motion capture, facial recognition, and voice recognition capabilities BIB002 . Which make the Kinect very useful in various application such as: object tracking and recognition BIB012 BIB007 BIB005 , human skeleton tracking and activity analysis BIB008 BIB007 BIB003 , hand gesture analysis BIB008 BIB001 , 3D-simultaneous localization and mapping BIB012 BIB005 , emergences detection such as: assault detection BIB013 BIB010 and fall detection BIB006 BIB011 BIB004 , and other. And so far, there have been only two generations of the Kinect, one for the Xbox One (Kinect v1) and one for Windows (Kinect v2), the first used on a gaming device, and the second used on the Windows machines. But in 2015, Microsoft announced that they will stop producing separate versions of the Kinect but will instead encourage developers to purchase the Kinect for Windows adapter instead to plug their Kinect into a PC . For the Kinect v2 with the Windows adapter, it's recommended to use the Kinect for Windows SDK 2.0 . The differences between the two generations of the Kinect camera can be shown in Table 2 [16, 19, 22-24].