A perception-enhancement network for accurate multi-person 2D pose estimation

To effectively solve the problems of human occlusion and motion blur in pose estimation algorithms, this paper proposes a bottom-up method for multi-person pose estimation based on human anchor joints and perception-enhancement networks. First, in terms of the detection task label, we divide human joint points into two groups—upper body and lower body—and then select two geometric anchor joint points from the two groups as joint point matching clues. Then, the other joint points of each group are represented by offset embedding of the joint point matching clues. Furthermore, two directional anchor joints that are rich in human orientation information are added to constitute a set of human anchor joints and form a new network detection target. Second, we design a perception-enhancement network based on the attention mechanism and feature fusion strategy, which can help the network effectively learn the unique features of each half-body and the inherent consistent features of the whole body. The proposed network has a stronger detection task modelling ability. In the test phase, based on the greedy strategy, the postprocessing algorithm is carried out to obtain the pose estimation results of multiple people by the final joint extraction and matching. The experimental results on the MPII dataset and CrowdPose dataset demonstrate the effectiveness of the proposed method. The code is open source and available online ( https://github.com/Ozone-oo/perception_enhancement_network.git ).