Indoor Sports To Human Detection And Tracking Algorithm To Achieve

This thesis is a part of "abnormal behavior recognizing and warning system", which aimed at monitoring the indoor environment. The major key technologies in intelligent video surveillance are integrated and the suit algorithms for the specific environment are chosen. So that, this system can detect and track the moving humans, analyze theirs behaviors, and recognize the abnormal behaviors accurately, such as fighting, for real-time monitoring. As a part of the system, the fundamental theories and key technologies of detection and tracking for moving people in indoor environment are researched. Using VC + + and OpenCV build experimental platform to realize the system's moving target detection module and tracking module.In this thesis, the system's main process is introduced. Then the hypothesis of human movement and the external environment are proposed for limiting the problem and the relevant background knowledge are described, including: color space choosing, moving human target detection and tracking technologies.On research of moving human detection, the traditional background subtraction method is introduced. Aimed at its shortages, such as loosing information, background building slowly and unreasonable update mechanism, an improved target detection method based on background difference is proposed. It processes the color image in sub-channel, including two stages: pixel-level and frame-level. Segment the foreground and background by adaptive threshold. The target region and background region are processed separately in background update step. Experiments show that this algorithm can overcome the defect above, extract moving target quickly and accurately. In addition, the thesis achieves shadow detection and remove, as well as the adhered human segmentation.On research of moving human tracking, some kinds of the characteristics used to describe the target are summarized at first. Then the Mean Shift and CamShift tracking algorithm are introduced, and their respective advantages disadvantages and application are discussed. On this basis, a joint multi-feature Mean Shift tracking algorithm is proposed. The target model combines two features of the candidate which have best ability for description target, and weighted the characteristics' credibility. It solves the problem of tracking failure in some similar color objects around. Through the experiment, with the traditional Mean Shift tracking algorithm, this algorithm has better effect in tracking accuracy and robustness.