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arXiv · 1905.05355

A Context-and-Spatial Aware Network for Multi-Person Pose Estimation

Dongdong Yu·Kai Su·Xin Geng·Changhu Wang
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Abstract

Multi-person pose estimation is a fundamental yet challenging task in computer vision. Both rich context information and spatial information are required to precisely locate the keypoints for all persons in an image. In this paper, a novel Context-and-Spatial Aware Network (CSANet), which integrates both a Context Aware Path and Spatial Aware Path, is proposed to obtain effective features involving both context information and spatial information. Specifically, we design a Context Aware Path with structure supervision strategy and spatial pyramid pooling strategy to enhance the context information. Meanwhile, a Spatial Aware Path is proposed to preserve the spatial information, which also shortens the information propagation path from low-level features to high-level features. On top of these two paths, we employ a Heavy Head Path to further combine and enhance the features effectively. Experimentally, our proposed network outperforms state-of-the-art methods on the COCO keypoint benchmark, which verifies the effectiveness of our method and further corroborates the above proposition.

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Dongdong Yu, Kai Su, Xin Geng, Changhu Wang. 2019-05-14. A Context-and-Spatial Aware Network for Multi-Person Pose Estimation. https://arxiv.org/abs/1905.05355

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