Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> Tracked Joints <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> BIB001 </s> Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey <s> Tracked Joints <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> BIB002
The supported joints are the following: The supported joints are the following: The Kinect official SDK 2.0 is recommended to track the users' skeletons for several reasons: 1. Kinect official SDK is more advantageous than other libraries when requiring skeleton tracking BIB002 . It provides full body tracking include head, hands, feet, clavicles. It detects 25 joints per people BIB001 which give more flexibility and options when you develop FD approach.