Occluded human pose estimation network based on knowledge sharing
A new estimation network was proposed for improving the insufficient occlusion handling ability of existing human pose estimation methods.An occluded parts enhanced convolutional network(OCNN)and an occluded features compensation graph convolutional network(OGCN)were included in the proposed network.A high-low order feature matching attention was designed to strengthen the occlusion area features,and high-adaptation weights were extracted by OCNN,achieving enhanced detection of the occluded parts with a small amount of occlusion data.OGCN strengthened the shared and private attribute compensation node features by eliminating the obstacle features.The adjacency matrix was importance-weighted to enhance the quality of the occlusion area features and to improve the detection accuracy.The proposed network achieved detection accuracy of 78.5%,67.1%,and 77.8%in the datasets COCO2017,COCO-Wholebody,and CrowdPose,respectively,outperforming the comparative algorithms.The proposed network saved 75%of the training data usage in the self-built occlusion dataset.
human pose estimationocclusion handlinghigh-low order feature matchingnode feature com-pensationadjacency matrix weighting
万方数据
维普
