Complex Human Pose Estimation via Keypoints Association Constraint Network

Human pose estimation has attracted enormous interest in the field of human action recognition. When the human pose is complex (such as pose distortion, pose reversal, etc.) or there is background interference (multi-target, shadow, etc.), the keypoints obtained by existing methods of human pose estimation often have incorrect positioning, category, and connection. This paper proposes a novel human pose estimation network KACNet via the keypoint association constraints. The Channel-1 of KACNet is constrained by the distance loss function to obtain the position of keypoints, and the Channel-2 of KACNet is constrained by the association loss function to obtain the relationship of keypoints. Then, the position and relationship of keypoints are fused by the weighted loss function to obtain the keypoints with accurate location, classification, and connection. Experiments on a large number of public datasets and Internet data show that our method can effectively suppress background interference to improve the accuracy of complex human pose estimation. Compared with state-of-the-art human pose estimation methods, the proposed methods can accurately locate, classify, and connect the human body keypoints robustly.