Driver Behaviour Detection Based on Convolution Neural System

Abstract

Abstract: Driving is a set of behaviours that need high concentration. Sometimes these behaviours are dominated by other acts such as smoking, eating, drinking, talking, phone calls, adjusting the radio, or drowsiness. These are also the main causes of current traffic accidents. Therefore, developing applications to warn drivers in advance is essential. This research introduces a light-weight convolutional neural network architecture to recognize driver behaviours, helping the warning system to provide accurate information and to minimize traffic collisions. This network is a combination of feature extraction and classifier modules. The feature extraction module uses the advantages of the standard convolution layers, depth wise separable convolution layers, average pooling layers, and proposed adaptive connections to extract the feature maps. The benefit of the convolution block attention module is deployed in the feature extraction module that guides the network in learning the salient features. The classifier module is comprised of a global average pooling and soft max layer to calculate the probability of each class. The overall design optimizes the network parameters and maintains classification accuracy. The entire network is trained and evaluated on three benchmark datasets.