Trajectory prediction combined with FairMOT for multi-object tracking

Aiming at the problems of ID switching and tracking performance degradation caused by frequent occlusion and similar appearance of the tracked objects in dense scenes, a multi-object tracking method named TPFairMOT based on trajectory prediction and FairMOT is proposed in this paper. In the trajectory prediction branch, the object position of the future frame is predicted by using the object bounding box of the past frame and the velocity information learning network parameters, which overcomes the prediction failure caused by the uncertain motion state after the object is occluded in the tracking process. Secondly, the joint learning framework is used to combine the trajectory prediction branch with the detection and re-identification branch, and the tracking error caused by the high similarity between multiple objects in the tracking process is solved by integrating the appearance features and motion features of the tracked objects. Finally, MOTChallenge benchmarks (IDF1, IDs, MOTA, MT, and ML) are introduced to evaluate TPFairMOT, and different trajectory prediction strategies (FairMOT_KF and TPFairMOT_RNN) are used on FairMOT and TPFairMOT for comparative analysis. It is proved that the accuracy and ID switching times of trajectory prediction in this paper are better than other strategies. In addition, TPFairMOT, TPFairMOT_RNN, and FairMOT were compared on the public data sets MOT16, MOT17, and MOT20. The results show that TPFairMOT reduces the number of ID switching when the object is occluded, maintains the long-term validity of the identity information, and demonstrate good anti-occlusion performance.