Occluded Part-aware Graph Convolutional Networks for Skeleton-based Action Recognition

Min Hyuk Kim, Min Ju Kim, Seok Bong Yoo

Abstract: Recognizing human action is one of the most critical factors in the visual perception of robots. Specifically, skeletonbased action recognition has been actively researched to enhance recognition performance at a lower cost. However, action recognition in occlusion situations, where body parts are not visible, is still challenging.We propose an occluded part-aware graph convolutional network (OP-GCN) to address this challenge using the optimal occluded body parts. The proposed model uses an occluded part detector to identify occluded body parts within a human skeleton. It is based on an autoencoder trained on a nonoccluded human skeleton and exploits the symmetry and angular information of the skeleton. Then, we select an optimal group constructed considering the occluded body parts. Each group comprises five sets of joint nodes, focusing on the body parts, excluding the occluded ones. Finally, to enhance interaction within the selected groups, we apply an interpart association module, considering the fusion of global and local elements. The experimental results reveal that the proposed model outperforms others on the occluded datasets. These comparative experiments demonstrate the effectiveness of the study in addressing the challenge of action recognition in occlusion situations. Our code is publicly available at https://github.com/MJ-Kor/OP-GCN.