Detection of Distracted Driver Using Convolutional Neural Network

Abstract

Abstract: Abnormal driving behaviour detection helps to ensure safety of driver and passenger. Recent studies have concluded that talking on phone while driving distracts driver attention upto 20%, which leads to accidents. Deep learning models can be used to find these distracted actions. In this system the abnormal behaviour of the driver like reaching behind, hair and makeup, drinking, texting etc. are detected through deep learning. Densely connected Convolutional Neural Networks, Residual Networks are used for detection. AWGRD model, the most sophisticated model of ResNet, is formedby superpositions of previous layers, and is used to detect the driver behaviour. The input to the proposed system will be a video file. The video input can be a real time live video or it can be uploaded. In output the system detects the activity or behaviour of the driver and warns them.