Perception-Enhanced Network for Accurate Human Pose Estimation

Abstract

Human pose estimation in computer vision is particularly challenging with images containing multiple individuals. Existing methods often integrate spatial and channel attention by simply adding them up through a cascade or parallel connection. The features extracted in this way could lead to less accurate key point predictions, especially in cases where the limbs of different people are obstructed or tangled with each other. To tackle this crucial issue, we develop a novel network that enhances key point detection by combining the spatial and channel attention in a more effective manner. Specifically, our network features a lightweight perception-enhanced module (PEM) that adaptively fuses spatial and channel features through a Hadamard product, thereby refining the overall feature representation. Moreover, by exploiting the initial feature map as a guide to generate the pixel attention, we further boost key point prediction accuracy. Extensive experimental results show that our developed network can clearly outperform the current state-of-the-art methods.