Incorporating statistical background model and Joint Probabilistic Data Association filter into motorcycle tracking

Multi-target tracking is an attractive research field due to its widespread application areas and challenges. Every point tracking method includes two mechanisms: object detection and data association. This paper is a combination between a statistical background modeling method for foreground object detection and joint probabilistic data association filter (JPDAF) in the context of motorcycle tracking. A major limitation of JPDAF is its inability to adapt to changes in the number of targets, but in this work, it is modified so that we can successfully apply JPDAF with known number of targets at each time instant. The experimental system works well with the number of targets less than 10/frame and be able to self-evolve with gradual and ldquoonce-offrdquo background changes.