SBF: Augmenting Skeleton for Effective Video-based Human Action Recognition
A novel and effective representation is presented that captures action-related information in the pipeline of HAR without any extra annotation overhead beyond the existing skeleton extraction and achieves significantly higher HAR accuracy with similar compactness and efficiency as compared with the state-of-the-art skeleton-only approaches.
Zhuoxuan Peng, Yi-Yi Ding, Yang‐Ming Lin et al.
· 0 citations