Driver behavior recognition based on dual-branch and deformable convolutional network method

Abstract

Aiming at the task of driver behavior recognition in the car cockpit, this paper proposes a recognition method based on a dual-branch neural network. The main branch of the network model uses ResNet50 as the backbone network for feature extraction, and uses deformable convolution to adapt the model to the shape and position changes of the driver in the image. The auxiliary branch assists in updating the parameters of the backbone network during the gradient backpropagation process, so that the backbone network can better extract features that are conducive to driver behavior recognition, thereby improving the recognition performance of the model. The ablation experiment and comparative experiment results of the network model on the State Farm public dataset show that the recognition accuracy of the proposed network model can reach 96.23%, and the recognition effect is better for easily confused behavior categories. The research results are of great significance for understanding driver behavior in the car cockpit and ensuring driving safety.