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arXiv · 1810.08338

Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking

Hengkai Guo·Tang Tang·Guozhong Luo·Riwei Chen·Yongchen Lu·Linfu Wen
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Abstract

Multi-person human pose estimation and tracking in the wild is important and challenging. For training a powerful model, large-scale training data are crucial. While there are several datasets for human pose estimation, the best practice for training on multi-dataset has not been investigated. In this paper, we present a simple network called Multi-Domain Pose Network (MDPN) to address this problem. By treating the task as multi-domain learning, our methods can learn a better representation for pose prediction. Together with prediction heads fine-tuning and multi-branch combination, it shows significant improvement over baselines and achieves the best performance on PoseTrack ECCV 2018 Challenge without additional datasets other than MPII and COCO.

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Hengkai Guo, Tang Tang, Guozhong Luo, Riwei Chen, Yongchen Lu, Linfu Wen. 2018-10-19. Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking. https://doi.org/10.1007/978-3-030-11012-3_17

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