A Simple Baseline for Pose Tracking in Videos of Crowed Scenes
By Li Yuan, Shuning Chang, Shuning Chang, Ziyuan Huang, Yichen Zhou, Yupeng Chen, Xuecheng Nie, Francis E. H. Tay, Jiashi Feng, Shuicheng Yan. 2020
4 citations recorded by the source corpus.
This paper presents our solution to ACM MM challenge: Large-scale Human-centric Video Analysis in Complex Events[13]; 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.
Subfields: Computer Vision and Pattern Recognition, 3D Vision & Pose
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Citations in the corpus
- VOLO: Vision Outlooker for Visual Recognition (2021, neutral): “vision task [22], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41].”
- Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet (2021, methodology): “Self-attention in CNNs Self-attention mechanism has been widely applied to CNNs in vision task [38, 57, 19, 47, 20, 39, 1, 6, 18, 31, 42, 13, 50, 48].”