DiffusionMOT: A Diffusion-Based Multiple Object Tracker

Yaxuan Hu, Jie Hua, Zhen Han, Hua Zou, Gang Wu, Zhongyuan Wang

Abstract: Recently, researchers have introduced diffusion models into multiple object tracking (MOT) tasks. However, existing diffusion-based MOT methods, such as DiffusionTrack, have significant limitations, including frequent ID switching, reduced performance when tracking nonlinear motion objects, and long inference time. To this end, we propose a more effective diffusion-based multiple object tracker named DiffusionMOT. In particular, we propose a mixed intersection over union (IoU) and Re-Identification (ReID) method for trajectory matching, which effectively reduces incorrect matches. Meanwhile, we propose a secondary calibration method for trajectory boxes, improving the accuracy of the generated detection boxes. Moreover, we introduce the parallel sampling technique from the field of image generation into object tracking and propose a parallel sampling module to enhance the model’s inference speed while maintaining tracking accuracy. Furthermore, we design a pair-based two-stage matching (PTM) pipeline to more effectively utilize potential detection information. Extensive experiments on several public MOT benchmarks, including DanceTrack, SportsMOT, MOT20, and MOT17, demonstrate that our approach achieves state-of-the-art (SOTA) performance. The code and models are available at https://github.com/sad123-yx/DiffusionMOT

External IDs:dblp:journals/tnn/HuHHZWW25