Robust tracking method based on particle filter for crossing of targets with similar appearances

Tracking methods based on the particle filter frequently use the appearance information of the target object to calculate the likelihood. The method using it often fails in tracking when the target object intersects with other objects with similar appearances. We propose a new approach for tracking objects with similar patterns in a video sequence taken by a moving camera. The proposed method based on the particle filter is robust to the intersection with other objects. Two state transition functions are defined for robust tracking. The method changes the function depending on the situation. In addition, the likelihood is calculated by using four factors which are the information of the color, the velocity and the distance between the objects, and the values calculated by the probability background model. Results are demonstrated by experiments using real video sequences.