Particle filter-based Multi-part Human Tracking in Video Sequences

• Main Authors: Li Jin-Han, 李金翰
• Other Authors: Hsi-Jian Lee
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/12545646111919340147

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 95 === The purpose of this thesis is to construct a specific person tracking system when given a image with any person. In the surveillance environment, with the progress of the times to process the video signals is more popular .So we think that the VASM is great and satisfies the need of the popular. In tradition we need watch the surveillance system in order to detect the invader non-automatically. So we combine the technology of the image process and the technology of tracking target to support the automatic machine for surveillance automatically In our system, we have four part: detection of moving target in video sequences, decomposing the human body into three block and track each block , abnormal detection, state correction. In the first stage, human detection, we use the foreground detection procedure to detect moving persons in a video. In the foreground detection we assume have solve the shadow problem and while tracking we have know the position of the target we track. In addition, our system is based on the tracking of only one part so we don’t consider the interaction between different blocks, each body is tracked independently. In the second stage, human body decomposition, after detection and separation of the moving suspect in a video sequence, we need to cur the human body into three blocks. Since the weights for matching the body parts may differ, we decompose the human body into three parts. Here, we propose how to cut the human body with three parts. In the third stage, abnormal detection, after cutting the human body into three part we will track the block each so this section to introduce how to define the constraint of the information of three parts. Then when we test some samples, we can detect the abnormal of any block. In the forth stage, state correction, after step three, we will know which part occurs abnormal, so we determine which block need to adjust. In the other words, we use some simply rule to adjust the relative position of abnormal block, and track the new position continuously. In the experiments, we test cases of human tracking , body part segmentation and abnormal detection and state correction. The experimental results show that our system is very effective for human tracking.