Driver behavior recognition based on attention module and bilinear fusion network
Chenkai Ma, Hao Henry Wang, Jianing Li · 2022
Due to the small differences in driver distraction actions and the high similarity of some actions, in this paper we propose a distracted driver behavior recognition method (BACNN) based on convolutional neural network (CNN) using bilinear fusion network and combining attention mechanism with current mainstream algorithms of deep learning. In this paper, we use a driver dataset from State Farm for testing, and use 75% of this dataset for training and 25% for testing. The driver behavior pictures in the dataset are extracted using our specific convolutional neural network model for feature extraction and classified using a fully connected layer. Experiments demonstrate that this method has better recognition results compared to single-model network extracted features.