Chen, Y.-T.Y.-T.ChenCHU-SONG CHENYI-PING HUNG2020-06-112020-06-112006https://scholars.lib.ntu.edu.tw/handle/123456789/500657https://www.scopus.com/inward/record.uri?eid=2-s2.0-34247604098&doi=10.1109%2fICME.2006.262409&partnerID=40&md5=e177d70dfbac2db206bcb5355165ae26Background model and tracking became critical components for many vision-based applications. Typically, background modeling and object tracking are mutually independent in many approaches. In this paper, we adopt a probabilistic framework that uses particle filtering to integrate these two approaches, and the observation model is measured by Bhattacharyya distance. Experimental results and quantitative evaluations show that the proposed integration framework is effective for moving object detection. © 2006 IEEE.Bit error rate; Computer simulation; Computer vision; Integration; Mathematical models; Background modeling; Probabilistic framework; Quantitative evaluations; Object recognitionconference paper10.1109/ICME.2006.2624092-s2.0-34247604098