关键词: 铁路工人, 人体行为识别(HAR), 深度学习, 空洞卷积, 自注意力机制

Abstract:

In order to improve the construction safety factor of railway workers, the intelligent monitoring method based on HAR was used to estimate the action of railway workers in the construction process. The deep learning method of end-to-end automatic extraction of data features is applied to build a network to improve the accuracy of behavior recognition and model generalization. In view of the poor parallel ability and long convergence time of the recurrent neural network(CNN), a deep learning model combining cavity convolution and self-attention mechanism is proposed. The WISDM and MobiAct public datasets are used to identify the basic actions and fall and impact behaviors on the two datasets. The results show that compared with convolutional neural network(CNN), long-term and short-term memory(LSTM) network and deep convolutional LSTM neural network, the model has better recognition accuracy and performance, and can realize more accurate division of worker behavior.

Key words: railway workers, human activity recognition(HAR), deep learning, dilated convolution, self-attention mechanism