Simple action recognition based on spatio-temporal features

Lili Sun; Siying Chen; Hongyan Liu; Ziheng He; Yuchen Wang; Sisi Che; Shuo Wang

doi:10.1117/12.2640206

1 August 2022 Simple action recognition based on spatio-temporal features

Lili Sun, Siying Chen, Hongyan Liu, Ziheng He, Yuchen Wang, Sisi Che, Shuo Wang

Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 1225707 (2022) https://doi.org/10.1117/12.2640206
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China

Abstract

Considering the information of video data spatially and temporally, this paper proposes a novel method of simple action recognition based on spatio-temporal features. For each frame, the spatial feature sequence is built by the joint angle features and the joint distance features after the human skeleton is obtained by the lightweight OpenPose. The atomic action is classified via the spatial feature sequences from video frames. Then, the atomic action label sequences are used to train hidden Markov models (HMMs) so that the constructed models can suit each action. This approach presents the advantages of the full use of the spatial features and the excellent learning ability of HMM. Experiments on datasets demonstrate the accuracy in simple action recognition of the proposed method.

Citation Download Citation

Lili Sun, Siying Chen, Hongyan Liu, Ziheng He, Yuchen Wang, Sisi Che, and Shuo Wang "Simple action recognition based on spatio-temporal features", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 1225707 (1 August 2022); https://doi.org/10.1117/12.2640206

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