Detection and feature fusion of trajectory Poisson multi-Bernoulli mixture algorithms for visual multiobject tracking

In a video multi-object tracking scene, the problems of target occlusion, track crossing still exist. Limited by the quantity of the detector, the traditional association algorithms will lead to missing and false tracking and increase the number of target ID switch and trajectory fragmentation. Aiming at these problems, this paper proposes an optimized algorithm based on the framework of Trajectory Poisson Multi-Bernoulli Mixture Filter, combines the joint detection and embedding algorithm to output both targets’ detections and features, designs new association and update algorithm to improve the trajectory maintenance. The result shows that the proposed algorithm can effectively decrease the number of ID switch and maintain the trajectories when tracking with short-time occlusions and crossing. It is tested on MOT datasets and gets a positive effect compared with other algorithms.