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Viewpoint guided multi-stream neural network for skeleton action recognition

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Abstract

Skeleton-based human action recognition has attracted considerable attention and succeeded in computer vision. However, one of the main challenges for skeleton action recognition is the complex viewpoint variations. Moreover, existing methods may be prone to develop the complicated networks with large model size. To this end, in this paper, we introduce a novel viewpoint-guided feature by adaptively selecting the optimal observation point to deal with the viewpoint variation problem. Furthermore, we present a novel multi-stream neural network for skeleton action recognition, namely Viewpoint Guided Multi-stream Neural Network (VGMNet). In particular, by incorporating four streams from spatial and temporal information, the proposed VGMNet can effectively learn the discriminative features of the skeleton sequence.We validate our method on three famous datasets, i.e., SHREC, NTU RGB+D, and Florence 3D. On SHREC, our proposed method has achieved better performance in terms of accuracy and efficiency against the state-of-the-art approaches. Furthermore, the highest scores on Florence 3D and NTU RGB+D show that our method is suitable for real application scenario with edge computing, and compatible to the case of multi-person action recognition.

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Data availability

The datasets analysed during the current study are available in the following links.

\(\bullet \) SHREC:   http://www-rech.telecom-lille.fr/shrec2017-hand

\(\bullet \) NTU RGB+D:  https://rose1.ntu.edu.sg/dataset/actionRecognition

\(\bullet \) Florence 3D:  https://www.micc.unifi.it/resources/datasets/florence-3d-actions-dataset

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China Grant 61876042, and the Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515011493).

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Authors and Affiliations

  1. School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, China

    Yicheng He, Zixi Liang, Shaocong He & Ming Yin

  2. School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    Yonghua Wang & Ming Yin

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Correspondence to Ming Yin.

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He, Y., Liang, Z., He, S. et al. Viewpoint guided multi-stream neural network for skeleton action recognition. Multimed Tools Appl 83, 6783–6802 (2024). https://doi.org/10.1007/s11042-023-15676-4

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