HiEve ACM MM Grand Challenge 2020: Pose Tracking in Crowded Scenes

Lumin Xu, Ruihan Xu, Sheng Jin

Abstract: This paper tackles the challenging problem of multi-person articulated tracking in crowded scenes. We propose a simple yet effective top-down crowd pose tracking algorithm. The proposed method applies Cascade-RCNN for human detection and HRNet for pose estimation. Then IOU tracking and pose distance tracking are applied successively for pose tracking. We conduct extensive ablation studies on the recently released HiEve crowd pose tracking benchmark. Our final model achieves 56.98 Multi-Object Tracking Accuracy (MOTA) without model ensembling on the HiEve test set. Our team SimpleTrack won the 3rd place in the ACM MM'2020 HiEve Challenge.