Kinetic People Tracking And Characteristic Behavior Identifying

The intelligent visual system which marries kinetic people tracking with consequent behaviour identifying based on tracks, is always challenging front problem. The research generally consists of two parts, which are tracking people and identifying their behaviour.At present there were many methods for people tracking and behaviour identifying. Nevertheless, people motion consists of people segments movements, and the movements of the segment are complex. Those factors result in the multiplicity of people motion, and being uneasy to track accurately. The people behaviour identification, which used for understanding and describing people behavour, belongs to higher level task of computer vision, and has many difficulties.Based on former researches, this paper unites tracking with identifying, and develops an intelligent vision system for tracking people and identifying their feature behavoiur automatically. This system is meant to solving the following problems: real-time tracking, people model learning atomatically, tracking kinetic people with people model, and identifying their behaviour based on tracking. This paper is arranged as follows:We analyzed the domestic and oversea methods for tracking people, classified those methods into categories, and figured out why they were not fit for our system. Meanwhile, we analyzed the behaviour identification methods. based on that, we proposed our framework for tracking and identifying, and introduced the algorithm we used.Aiming at the weakness of classical background subtraction, we generalized the haar-like feature and integral image ever used for detecing face, and used them to detect different segments of people in video. For its natural characteristic, Haar-like features were suitable for localizing rectangle or rectangle-like region of the image, and we used integral image to position people parts with those features rapidly.We classified the points of color space using cluster method, and separated the detections from noise. Traditional cluster algorithm can not used in our system. We brought in non-parametric cluster method which grounds on kernel function, computed the gradient of points, moved the points towards mode iteratively, and assigned the point to one certain cluster. It means we classify the points while removing noise.Based on clustering, We learned the people model using probabilitic inferences. Traditional HMM was easy to lose target while tracking people, and resulted in the failure of learning. We modified the HMM, and made inferences with dynamic programming to obtain the optimal segment sequence, and searched for the template of that segment, using cluster algorithm. Then we extended the modified HMM making use of the geometric restriction of people, and builded an inference framework learning complete people model with video.We tracked kinetic people in video using learned people model. We tracked kinetic people by detecting people model in frame of video. By matching the people model to image, we detected the model in image, using a cost function as measure of match. For optimalizing the cost function, we used dynamic programming, and we introduced distance tansform for reducing the computation complexity. At the same time, we improved the selection of root node of people model, remarkedly abbreviated the search space of matching procrdure and speeded up the match of people model to frame of the video.We identified people behaviour with people pose of tracking stage. Using annotated motion library&people pose obtained by tracking, we builded a HMM for inference, and synthesized an optimal motion sequence which matched the tracked pose best, with Viterbi algorithm.For the motion sequence was annotated in advance, we can identify the people behaviour of video.Furthermore, we developed an identification system based on the pose tracked, aiming at the property of certain behaviour. It was effective and rapid, yet disadvantage in relatively cabined scope of application.