Recursive Clustering for Multiple Object Tracking

In this paper, we propose a method to track multiple deformable objects in video sequences using a recursive clustering scheme. In a first step, a set of Gabor filter banks is used to filter the difference image between two consecutive frames. Then, the moving areas are sampled by randomly positioning particles in high magnitude area of the filtered image. Finally, these points are clustered to obtain one class for each moving object. The novelty in our method is in using cluster information for the previous frame to classify new particles in the current frame. This makes our method robust to occlusions, objects entering and leaving the field of view, objects stopping and starting, and moving objects getting really close to each other.