Multi-Modal Human Action Recognition With Sub-Action Exploiting and Class-Privacy Preserved Collaborative Representation Learning

Chengwu Liang, Deyin Liu, Lin Qi, Ling Guan
2020 IEEE Access  
Multimodal human action recognition with depth sensors has drawn wide attention, due to its potential applications such as health-care monitoring, smart buildings/home, intelligent transportation, and security surveillance. As one of the obstacles of robust action recognition, sub-actions sharing, especially among similar action categories, makes human action recognition more challenging. This paper proposes a segmental architecture to exploit the relations of sub-actions, jointly with
more » ... eous information fusion and Class-privacy Preserved Collaborative Representation (CPPCR) for multi-modal human action recognition. Specifically, a segmental architecture is proposed based on the normalized action motion energy. It models long-range temporal structure over video sequences to better distinguish the similar actions bearing sub-action sharing phenomenon. The sub-action based depth motion and skeleton features are then extracted and fused. Moreover, by introducing within-class local consistency into Collaborative Representation (CR) coding, CPPCR is proposed to address the challenging sub-action sharing phenomenon, learning the high-level discriminative representation. Experiments on four datasets demonstrate the effectiveness of the proposed method. INDEX TERMS Action recognition, feature fusion, class-privacy preserved, sub-action sharing. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ VOLUME 8, 2020
doi:10.1109/access.2020.2976496 fatcat:357i4iqivzhjpitbgttmjix3ri