  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.
