Group Non-Critical Behavior Recognition Based on Joint Attention Mechanism of Sensor Data and Semantic Domain

As science and technology continue to advance, sensor technology is being used in more and more industries. However, traditional methods have the problem of ignoring the semantic information of individual behavior and the correlation between individuals and groups. Based on this, the study proposes a new method for group behavior recognition. The process of feature extraction is performed on group behavior by collecting sensor data and combining a semantic domain joint attention mechanism. This is achieved through the construction of a recognition method based on a data domain and semantic domain joint attention mechanism, which enables the accurate identification of non-critical behaviors in the group. The findings showed that, when the group members are constant, the hybrid network based on a convolutional neural network and bi-directional long and short-term memory network improved the F1 by 0.2% and the accuracy by 0.19%. Moreover, the hybrid network combining graph neural network, bi-directional long and short-term memory network, and convolutional neural network improved results. In group behavior recognition, group relationship modeling based on graph convolutional network improved F1 by 0.17% and accuracy by 0.17% compared to the hybrid network, indicating that group relationship modeling better captures group interaction features and improves recognition. The method is highly effective in the field of group behavior recognition and is expected to provide a new idea for monitoring and managing group behavior in practical scenarios.

@article{Li2024,
  title     = {Group Non-Critical Behavior Recognition Based on Joint Attention Mechanism of Sensor Data and Semantic Domain},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Chen Li and Baoluo Liu},
  doi       = {10.14569/IJACSA.2024.0150760},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150760}
}