Volume 41 Issue 5
Oct.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(5): 115-126
CAO Jingjing, YU Zhou, LI Pengfei, MIN Yanping, HUANG Qixian, ZHAO Qiangwei. A Recognition Model for Violent Sorting Activity Based on the ST-AGCN Algorithm[J]. Journal of Transport Information and Safety, 2023, 41(5): 115-126. doi: 10.3963/j.jssn.1674-4861.2023.05.012
Citation: CAO Jingjing, YU Zhou, LI Pengfei, MIN Yanping, HUANG Qixian, ZHAO Qiangwei. A Recognition Model for Violent Sorting Activity Based on the ST-AGCN Algorithm[J]. Journal of Transport Information and Safety, 2023, 41(5): 115-126. doi: 10.3963/j.jssn.1674-4861.2023.05.012
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A Recognition Model for Violent Sorting Activity Based on the ST-AGCN Algorithm

doi: 10.3963/j.jssn.1674-4861.2023.05.012

  • School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China

  • Received Date: 2022-09-04
    Available Online: 2024-01-18
    Abstract
  • An image-based behavior recognition method can be utilized to address the issue of violent sorting which is prevalent within the express logistics industry. However, this method presents challenges including algorithmic fragility and the difficulty in obtaining joint point data in practical scenarios. In response to these challenges, a video dataset is generated to capture instances of violent sorting behaviors in logistics, and a model is developed to identify such behaviors. Video data from both indoor and outdoor scenarios is collected, with real-time video image transmission achieved using the Python socket module. Screening rules are applied to eliminate non-standard data, and the OpenPose model is employed to obtain joint data. To address the limitation of general recognition network in reflecting the impact of joint points on actions, an optimized graph neural network is developed based on ST-GCN. The spatial attention mechanism is used to understand the influence of different joints on various movements, updating the weight of each joint. The topology and network parameters of the human bone map are optimized through end-to-end learning to emphasize the influence of key joints on action recognition. Comparative and ablation experiments are conducted on various deep learning models using violent sorting videos captured in indoor and outdoor environments. The experimental results indicate that the accuracy of ST-AGCN model for identifying violent sorting behavior in real scenes is 5.6% higher than ST-GCN. Compared with STA-LSTM, ST-AGCN without spatial attention mechanism, and ST-AGCN without the adaptive graph structure layer, the accuracy of ST-AGCN model is improved by 13.82%, 2.36%, and 1.61% respectively, which indicates the ST-AGCN model is also suitable for complex logistics sorting scenes in cluttered indoor and outdoor environments and partial occlusion, and verifies the superiority of ST-AGCN and the effectiveness of the spatial attention mechanism and the adaptive graph structure layer.

     

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