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In order to cope with the increasing demand for labeling data and privacyissues with human detection, synthetic data has been used as a substitute andshowing promising results in human detection and tracking tasks. We participatein the 7th Workshop on Benchmarking Multi-Target Tracking (BMTT), themed on"How Far Can Synthetic Data Take us"? Our solution, PieTrack, is developedbased on synthetic data without using any pre-trained weights. We propose aself-supervised domain adaptation method that enables mitigating the domainshift issue between the synthetic (e.g., MOTSynth) and real data (e.g., MOT17)without involving extra human labels. By leveraging the proposed multi-scaleensemble inference, we achieved a final HOTA score of 58.7 on the MOT17 testingset, ranked third place in the challenge.

PieTrack: An MOT solution based on synthetic data training and self-supervised domain adaptation