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