3DV: 3D Dynamic Voxel for Action Recognition in Depth Video

• Published in: Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 508 - 517
• Main Authors: Wang, Yancheng, Xiao, Yang, Xiong, Fu, Jiang, Wenxiang, Cao, Zhiguo, Zhou, Joey Tianyi, Yuan, Junsong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2020
• Subjects: Dynamics; Machine learning; Pattern recognition; Skeleton; Solid modeling; Three-dimensional displays; Two dimensional displays
• ISSN: 1063-6919
• DOI: 10.1109/CVPR42600.2020.00059

For depth-based 3D action recognition, one essential issue is to represent 3D motion pattern effectively and efficiently. To this end, 3D dynamic voxel (3DV) is proposed as a novel 3D motion representation manner. With 3D space voxelization, the key idea of 3DV is to encode the 3D motion information within depth video into a regular voxel set (i.e., 3DV) compactly, via temporal rank pooling. Each available 3DV voxel intrinsically involves 3D spatial and motion feature for 3D action description. 3DV is then abstracted as a point set and input into PointNet++ for 3D action recognition, in the end-to-end learning way. The intuition for transferring 3DV into the point set form is that, PointNet++ is lightweight and effective for deep feature learning towards point set. Since 3DV may loose appearance clue, a multi-stream 3D action recognition manner is also proposed to learn motion and appearance feature jointly. To extract richer temporal order information of actions, we also split the depth video into temporal segments and encode this procedure in 3DV integrally. The extensive experiments on the well-established benchmark datasets (e.g., NTU RGB+D 120 and NTU RGB+D 60) demonstrate the superiority of our proposition. Impressively, we acquire the accuracy of 82.4% and 93.5% on NTU RGB+D 120 with the cross-subject and cross-setup test setting respectively. 3DV's code is available at https://github.com/3huo/3DV-Action.