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Learning Shape-Motion Representations from Geometric Algebra Spatio-Temporal Model for Skeleton-Based Action Recognition
Yanshan Li, Rongjie Xia, Xing Liu, Qinghua Huang
Skeleton-based action recognition has been widely applied in intelligent video surveillance and human behavior analysis. Previous works have successfully applied Convolutional Neural Networks (CNN) to learn spatio-temporal characteristics of the skeleton sequence. However, they merely focus on the coordinates of isolated joints, which ignore the spatial relationships between joints and only implicitly learn the motion representations. To solve these problems, we propose an effective method to learn comprehensive representations from skeleton sequences by using Geometric Algebra. Firstly, a frontal orientation based spatio-temporal model is constructed to represent the spatial configuration and temporal dynamics of skeleton sequences, which owns the robustness against view variations. Then the shape-motion representations which mutually compensate are learned to describe skeleton actions comprehensively. Finally, a multi-stream CNN model is applied to extract and fuse deep features from the complementary shape-motion representations. Experimental results on NTU RGB+D and Northwestern-UCLA datasets consistently verify the superiority of our method.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Skeleton Based Action Recognition | NTU RGB+D | FO-GASTM | Accuracy (CS) | 82.83 | #104 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | FO-GASTM | Accuracy (CV) | 90.05 | #104 of 135 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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