HMM-based people counting

• Main Authors: Chen, Pin Han, 陳品翰
• Other Authors: Huang, Chung Lin
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/13179025336191393905

碩士 === 國立清華大學 === 電機工程學系 === 101 === This paper presents a new people counting approach using ellipse detection , HOG upper body detection, HMM and tracking. First of all, the foreground object silhouettes are extracted described as blobs. The linkage is generated by analyzing the blob information between blobs and the relationship between pedestrian. We can get the object blobs previous states by analyzing the linkage information in the region of interest. Then we have the information of the objects which are merged or separated. To count the objects in the blob, we use ellipse detection and HOG upper body detection to get the number of objects in the blob. We use ellipse detection by matching the area and fitting the outline of the blobs with the ellipse, and then we use HOG upper body detection to detect the position of the pedestrian if the blob information is not enough. To solve occlusion problem, we use HMM to model the variations blob states on each linkage, which can be used to find the most matching hypothesis to determine the people number. Different from previous methods, we analyze the image sequences from the objects entering the scene till the objects exiting the scene. Compare with the specific frames analyzing way, we have better accuracy. In the experiments, we illustrate the effectiveness of our method.