Research on human behavior recognition in factory environment based on 3-2DCNN-BIGRU fusion network

Zhenyu Wang, Jianming Zheng, Mingshun Yang, Weichao Shi, Yulong Su, Ting Chen, Chao Peng

Abstract: In the safety monitoring of workers in manufacturing enterprises, there is a problem of large amount of calculation in the identification model caused by multi-objective high concurrent behavior. In this paper, we propose a human behavior recognition model that combines multi-dimensional convolution and gated recurrent neural network from the perspective of model structure design. The single target human behavior data set of the factory was constructed by YOLOv7 target detection and BOT-SORT multi-target tracking. Human behavior recognition model 3-2DCNN-BIGRU, in the mixed spatio-temporal feature extraction layer, uses the advantages of 3DCNN in spatio-temporal feature extraction to extract spatio-temporal features; The 3-2DCNN is used to extract spatial features after dimension reduction to improve the computational complexity and reduce the complexity of the model; Using the idea of expansion convolution in the time convolution network, the receptive fields of 3DCNN and 3-2DCNN are increased, and the ability of spatio-temporal feature extraction of the model is enhanced. In the time feature enhancement layer, a bidirectional gated recurrent neural network is fused to enhance the model’s ability to extract time features, thereby improving the overall performance of the model. With fewer parameters, the accuracy on the Fall Dataset reaches 98.65%, which can effectively identify human behaviors such as walking, sitting, and falling in factories, ensuring the safety of workers.

External IDs:dblp:journals/sivp/WangZYSSCP25