Background 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.