关键词: 人体姿态估计, 关键点级遮挡, 数据增强, 人体结构损失

Abstract: Human pose estimation techniques have important applications in many fields. Existing studies focus on the precise localization of human keypoints without occlusion, but ignore the prevalent occlusion problem in the process of human image acquisition. To address this problem, a human pose estimation method based on keypoint data augmentation is proposed. Specifically, the data augmentation strategy generates a specific number and size of occlusion regions centered on the human visible keypoints in the training images to simulate the scenes when the human keypoints are occluded and to improve the robustness of the network model for keypoint prediction under the occlusion scenes. In order to improve the model’s perception of the correlation between the occluded keypoints and the adjacent keypoints, a loss function based on the priori knowledge of the human body structure is further designed to construct the adjacent keypoint connections based on the real structure of the human body and constrain the predicted keypoint coordinate range, so as to improve the coordinate accuracy of the occluded keypoints. The prediction results on the OCHuman test set and COCO validation set show that the method can improve the performance of human pose estimation in occluded scenes without increasing the network parameters compared with the benchmark network model.

Key words: human pose estimation, keypoint-level occlusion, data augmentation, human structure loss