Multiscale structure aware semantic GCN for occluded human pose estimation

In recent years, human pose estimation has seen substantial advancements. However, occlusion remains a persistent challenge, leading to issues like missing keypoints, ambiguity, and abnormal poses. This paper introduces a novel framework, comprising two core components: the Initial Pose Net and the Multi-Scale Structure-Aware Semantic GCN (MSS-GCN). Our approach starts with the Initial Pose Net, which predicts an initial pose with the aid of attention mechanisms, enhancing feature precision. Subsequently, the MSS-GCN refines this initial pose to yield the final pose. The MSS-GCN is a network consisting of multiple parallel multi-scale subgraphs, informed by the human body's geometric constraints. It leverages prior knowledge of human anatomy to learn constraints regarding keypoints and uses feature information to compensate for the absence of semantic data in coordinates. The MSS-GCN excels in capturing the structural aspects of the human body and effectively handles the challenge of long-distance keypoint predictions. Additionally, our module is designed for easy integration into other networks, enhancing its versatility. Through extensive experiments on standard benchmark datasets for occluded human pose estimation, we demonstrate that our method surpasses existing state-of-the-art approaches. This work marks a significant stride toward enhancing pose estimation accuracy in occlusion scenarios.