Summary of Skeleton-based Action Recognition with Spatial-structural Graph Convolution, by Jingyao Wang et al.
Skeleton-Based Action Recognition with Spatial-Structural Graph Convolution
by Jingyao Wang, Emmanuel Bergeret, Issam Falih
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to Skeleton-based Human Activity Recognition (HAR) using Graph Convolutional Network (GCN). It addresses the limitations of traditional GCN methods, particularly over-smoothing and underutilization of edge nodes. The proposed Spatial-Structural GCN (SpSt-GCN) model consists of two streams: spatial GCN, which aggregates information based on the topological structure of the human body, and structural GCN, which differentiates node sequences based on their similarity. The spatial connection is fixed, while the structural connection is dynamic and depends on the type of movement. This approach allows for increased flexibility and achieves good results on two large-scale datasets: NTU RGB+D and NTU RGB+D 120. The method’s efficiency and effectiveness make it a promising solution for HAR applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about recognizing human activities, like walking or running, using computer algorithms. It wants to improve the way these algorithms work by paying attention to the parts of our bodies that move when we do certain actions. The new approach uses two kinds of connections: one that stays the same no matter what we’re doing and another that changes depending on the movement. This helps the algorithm understand activities better and make more accurate predictions. The researchers tested their method on lots of data and found it worked well, making it a useful tool for recognizing human activities. |
Keywords
» Artificial intelligence » Activity recognition » Attention » Convolutional network » Gcn
