Behavior recognition method based on short-term spatiotemporal information fusion attention

Jiahao Liu

doi:10.1117/12.3055792

9 January 2025 Behavior recognition method based on short-term spatiotemporal information fusion attention

Jiahao Liu

Author Affiliations +

Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 134860R (2025) https://doi.org/10.1117/12.3055792
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China

Abstract

In recent years, Graph Convolutional Networks (GCNs) have gained attention in the field of action recognition. However, existing methods can only extract simple spatiotemporal features of individual joints and fail to capture comprehensive spatiotemporal information of the entire human body, with limitations in modeling short-term spatiotemporal information. To address these issues, this paper proposes a Graph Convolutional Network method with short-term spatiotemporal information fusion and attention. This method learns temporal features through a short-term spatiotemporal feature fusion module, enhances the temporal representation of action features by combining human spatiotemporal information, and improves spatial skeleton information through keypoint attention modeling. Finally, multi-scale temporal convolution is used for long-term information exchange, and fusion of four-stream scores is employed for classification prediction. Experimental results demonstrate that this method outperforms existing approaches on the NTU RGB+D and NTU RGB+D120 datasets.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiahao Liu "Behavior recognition method based on short-term spatiotemporal information fusion attention", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 134860R (9 January 2025); https://doi.org/10.1117/12.3055792

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