Vehicle Turn-Signal Detection for Automated Flagging Systems

Abstract

Flaggers are always required to work close to open traffic lanes. They may be hit by distracted, speeding, or intoxicated drivers, leading to injuries and fatalities. To protect them, the concept of an automated flagging system device (AFSD) has been proposed. One of the core functions of an AFSD is to recognize the turn signals of vehicles in the lanes in order to guide the traffic. However, existing studies on vehicle turn-signal recognition have mainly focused on autonomous driving scenarios. Research on vehicle turn-signal recognition for AFSDs has been limited. In this study, we propose a novel method for vehicle turn-signal recognition using a video camera on an AFSD. The method first uses object detection and tracking to locate the vehicles and identify their front lighting areas (FLAs). Then, the luminance of each vehicle’s FLA is extracted. Based on the captured luminance features over time, a convolutional operator is applied to figure out whether the left or right FLA is flashing. The proposed method was implemented and tested on real traffic videos. The results showed that the overall signal recognition accuracy of the method reached 78.62%.