Driving Behavior Analysis Algorithm Based on Convolutional Neural Network

This paper proposes a driving behavior analysis algorithm based on convolutional neural network, which realizes the driver's fatigue detection and behavior detection based on face location. Aiming at fatigue detection task, this paper explores the influences of different receptive fields of convolutional neural network on the accuracy of fatigue detection and obtains the optimal structure of the fatigue detection model. For the behavior detection task, considering that the corresponding scope sizes of different behaviors arc different, a multi-branch attention network model based on multi-scale features is proposed, which realizes multi-scale classification by extracting multi-scale features and exploits attention mechanism to strengthen distinguishing features. Experimental results show that this method can be combined with a variety of mainstream convolutional neural network models and effectively improves the accuracy of driving behavior analysis.