DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions
Teng Fu Xiaocong Wang Haiyang Yu Ke Niu Bin Li Xiangyang Xue
Multiple object tracking (MOT) tends to become more challenging when severe
occlusions occur. In this paper, we analyze the limitations of traditional
Convolutional Neural Network-based methods and Transformer-based methods in
handling occlusions and propose DNMOT, an end-to-end trainable DeNoising
Transformer for MOT. To address the challenge of occlusions, we explicitly
simulate the scenarios when occlusions occur. Specifically, we augment the
trajectory with noises during training and make our model learn the denoising
process in an encoder-decoder architecture, so that our model can exhibit
strong robustness and perform well under crowded scenes. Additionally, we
propose a Cascaded Mask strategy to better coordinate the interaction between
different types of queries in the decoder to prevent the mutual suppression
between neighboring trajectories under crowded scenes. Notably, the proposed
method requires no additional modules like matching strategy and motion state
estimation in inference. We conduct extensive experiments on the MOT17, MOT20,
and DanceTrack datasets, and the experimental results show that our method
outperforms previous state-of-the-art methods by a clear margin.