ReIDTrack: Multi-Object Track and Segmentation Without Motion
Kaer Huang Bingchuan Sun Feng Chen Tao Zhang Jun Xie Jian Li Christopher Walter Twombly Zhepeng Wang
In recent years, dominant Multi-object tracking (MOT) and segmentation (MOTS)
methods mainly follow the tracking-by-detection paradigm. Transformer-based
end-to-end (E2E) solutions bring some ideas to MOT and MOTS, but they cannot
achieve a new state-of-the-art (SOTA) performance in major MOT and MOTS
benchmarks. Detection and association are two main modules of the
tracking-by-detection paradigm. Association techniques mainly depend on the
combination of motion and appearance information. As deep learning has been
recently developed, the performance of the detection and appearance model is
rapidly improved. These trends made us consider whether we can achieve SOTA
based on only high-performance detection and appearance model. Our paper mainly
focuses on exploring this direction based on CBNetV2 with Swin-B as a detection
model and MoCo-v2 as a self-supervised appearance model. Motion information and
IoU mapping were removed during the association. Our method wins 1st place on
the MOTS track and wins 2nd on the MOT track in the CVPR2023 WAD workshop. We
hope our simple and effective method can give some insights to the MOT and MOTS
research community. Source code will be released under this git repository