CVPR 2023 – MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors

In this episode we discuss MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors
by Yuang Zhang, Tiancai Wang, Xiangyu Zhang. The paper proposes a new pipeline, called MOTRv2, that improves end-to-end multi-object tracking by incorporating an extra object detector. The pipeline first adopts an anchor formulation of queries and then uses the detector to generate proposals as anchors, providing detection prior to MOTR. This improves detection performance and eases the conflict between joint learning detection and association tasks in MOTR. MOTRv2 achieved state-of-the-art performance on the BDD100K dataset and ranked 1st in the 1st Multiple People Tracking in Group Dance Challenge. Code is available on GitHub.