This paper presents our solution to ACM MM challenge: Large-scale Human-centric Video Analysis in Complex Events\cite{lin2020human}; specifically, here we focus on Track3: Crowd Pose Tracking in Complex Events. Remarkable progress has been made in multi-pose training in recent years. However, how to track the human pose in crowded and complex environments has not been well addressed. We formulate the problem as several subproblems to be solved. First, we use a multi-object tracking method to assign human ID to each bounding box generated by the detection model. After that, a pose is generated to each bounding box with ID. At last, optical flow is used to take advantage of the temporal information in the videos and generate the final pose tracking result.
@article{arxiv.2010.10007,
title = {A Simple Baseline for Pose Tracking in Videos of Crowded Scenes},
author = {Li Yuan and Shuning Chang and Ziyuan Huang and Yichen Zhou and Yunpeng Chen and Xuecheng Nie and Francis E. H. Tay and Jiashi Feng and Shuicheng Yan},
journal= {arXiv preprint arXiv:2010.10007},
year = {2020}
}
Comments
2nd Place in ACM Multimedia Grand Challenge: Human in Events, Track3: Crowd Pose Tracking in Complex Events. ACM Multimedia 2020. arXiv admin note: substantial text overlap with arXiv:2010.08365, arXiv:2010.10008