Research on a Multi object Tracking Model Based on Bayesian Algorithm

In response to the problems of weak timeliness and large errors in traditional algorithms in complex scenes, a Bayesian based multi-target tracking model (BFMTT) was designed by introducing the theory of random finite sets and combining it with probability hypothesis density filters. This model effectively addresses the tracking of targets in environments with varying numbers of targets, occlusion, and clutter. The results indicate that the model has significant advantages in tracking accuracy and real-time performance for multiple targets in complex scenes.