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Human Tracking in a video sequence is an important task in civilian surveillance. Successful human tracking provides data for security-purposes. However, human tracking in video sequences is always a challenging problem. Due to the rapid changes in shape with irregular motion, typical methods may not have good results, especially under occlusion and interaction. Recently, methods based on multiple cameras have been proposed. However, this requires a high computation cost. In the stereo cameras approach, 3D information is obtained and projected onto an occupancy map. In this paper, we propose an algorithm combining the occlusion and interaction model and Markov Chain Monte Carlo (MCMC) such that humans can be tracked under frequent interaction and occlusion. We have successfully reduced the number of failures by 78%. We present an efficient and effective algorithm under frequent interaction and occlusion.
Note: As originally published there was an error in this document. Due to a production error the author names were not published correctly. "Pak Ming, Cheung" and "Kam Tim, Woo" were intended to read as: "Pak Ming CHEUNG" and "Kam Tim WOO." The metadata has been updated but the PDF remains unchanged.