OSD: An Occlusion Skeleton Dataset for Action Recognition

Currently available 2D skeleton datasets for action recognition mostly contain nonoccluded skeleton samples. Models trained on such datasets lack generalization ability in occlusion situations. In this paper we propose an occlusion projection method, which projects a 3D occlusion object into 2D plane to generate a 2D occluded area. Based on this method, we build a 2D occlusion skeleton dataset named OSD with 56,800 occluded skeleton samples and 60 distinct classes. Experimental results show that the model trained on OSD has better generalization ability in occlusion situations compared with the model trained on datasets with nonoccluded samples, which proves the effectiveness of OSD.