AETrack: An Efficient Approach for Online Multi-Object Tracking

Tracking by detection(TBD) method has achieved great improvements for its high efficiency, extensibility and portability, but it still struggles on computational efficiency. Many recently proposed methods improve performance by integrating appearance similarity and simply extract appearance feature for all the targets. This results redundant calculations as some targets can already be easily tracked without feature extraction, such as targets walking alone. In this work, we tackle the efficiency problem from a new perspective and propose AETrack, an efficient approach for online multi-object tracking(MOT), which integrates three association metrics through a novel cascaded matching strategy. Instead of simply computing all the association metrics for all tracklets, our matching strategy dynamically chooses and fuses the metrics for each tracklet considering both effectiveness and efficiency. Inference speed is boosted greatly and accuracy is still competitive. AETrack achieves 64.7 HOTA on MOT17 test set while running at 58 FPS and 62.8 HOTA on MOT20 at 52 FPS. Our code and models will be public soon.
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