Multiple Objects Trackingwith Multiple Hypotheses Dynamic Updating
We present a novel and robust multi-object tracking algorithm based on multiple hypotheses about the trajectories of the objects. We represent the trajectories of the objects by a set of path graphs in which the path graphs that have the closest temporal relationship with the current frame are stored in a buffer. New hypotheses about the trajectories of the objects are continually generated based upon the spatial and temporal information of the objects. The novelty of our multi-object tracking algorithm lies in our framework in which we update these hypotheses by exploiting information in later frames and dynamically relating this information to the current set of path graphs in the buffer. Our experiments show that even with a small buffer size, our multi-object tracking algorithm achieves more than 75% accuracy in the tracking results of our test video sequences. Furthermore, we demonstrate that by a small increase of the buffer size, we are able to improve the tracking accuracy in the video sequences to above 90%.