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Article type: Research Article
Affiliations: Mechanical & Electrical Department, Guangzhou Institute of Technology, Guangzhou, China
Correspondence: [*] Corresponding author. Long Luo, Email: [email protected].
Abstract: This paper proposes a lightweight human action recognition algorithm for pedestrian behavior recognition. First, the skeleton feature information is input into the HRNet network model. In order to selectively enhance more details containing the target features and suppress irrelevant or weak features, an external attention mechanism is added to the HRN child model. Secondly, in order to extract the temporal characteristics of the target feature vector and ensure the continuity of actions in human behavior recognition, a dual-stream network based on HRNet and Long Short-Term Memory (LSTM) is constructed; finally, due to the huge model, it cannot be well transplanted to embedded. Therefore, this paper uses depthwise separable convolution to lightweight the network model. The experimental results show that in terms of human behavior recognition, the method in this paper has better recognition accuracy than Two-stream, Multi-streamCNN, Cov3DJ, ConvNets, JTM, ASM-3, RF+SW, hd-CNN and TPSMMs.
Keywords: External attention mechanism, lightweight, the network model, depthwise separable convolution, dual-stream network introduction
DOI: 10.3233/JIFS-239704
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
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