ViPose: Keypoint Visibility-Based Human Pose Estimation

Human Pose Estimation (HPE) aims to predict the positional coordinates of body keypoints in images. While significant progress has been made in HPE, certain challenges persist. For example, the potential for robust occlusion can result in considerable confidence in predicting false-positive keypoints. This can cause problems in applications where high detection accuracy is required, such as in a traffic environment. Most existing methods focus on predicting the positional coordinates of each human joint, but they fail to consider the keypoint occlusion problem and detect the visibility of keypoints in the image. In this paper, we propose a visibility-guided human pose estimation model, named ViPose. We extract the visibility information of keypoints to detect whether a specific keypoint of the subject in the image is obscured by other objects, visible or not captured. ViPose is composed of two modules: HPE keypoint coordinate predictor module, and keypoint visibility detector module. To verify the effectiveness of ViPose, we conduct experiments on both the general MSCOCO dataset and the diver monitoring DriPE dataset. The experimental results show that ViPose can achieve good performance with fewer parameters.