Abnormal Driving Detection In Video Using Alternative Wide Group Residual Densely

Abstract

Video-based abnormal driving behavior detection is becoming increasingly popular for the time being. It is highly important in ensuring the safety of drivers and passengers in the vehicle, and it is an essential step in realizing automatic driving at the current stage. Thanks to recent developments in deep learning techniques, this challenging detection task can be largely facilitated via the prominent generalization ability of advanced deep learning models and large volumes of video clips that are indispensable for thoroughly training these data-driven deep learning models. Deep ability combination strategies are careworn, and three precise deep gaining knowledge of-based combination variations are inspired by using the days proposed. Famous Alternative wide group residual densely and densely connected convolution network (DenseNet) is provided to meet the video-primarily based unusual riding movement's discovery activity for the first time.