Driver Behaviors Recognizer Based on Light-weight Architecture and Attention Mechanism

Abstract

Driving is a set of behaviors that need high concentration. Sometimes these behaviors 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 behaviors, 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, depthwise 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 softmax 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: the State Farm Distracted Driver Detection, the American University in Cairo version 1, and the American University in Cairo version 2. As a result, the accuracies on overall classes (ten classes) are 99.95%, 95.57%, and 99.61%, respectively. Also, several video tests with VGA (Video Graphics Array), HD (High Definition), and FHD (Full High Definition) resolution were conducted, and they can be seen at https://bit.ly/3GY2iJl .