A computer vision-based deep learning model to detect wrong-way driving using pan-tilt-zoom traffic cameras

Hundreds of fatal accidents occur each year due to wrong-way driving (WWD). Although several methods have been developed to detect WWD using existing closed-circuit television (CCTV) data, they all require manual recalibration whenever a camera rotates, and are thus not scalable across statewide CCTV networks. This paper, therefore, proposes an end-to-end deep-learning-based model that considers camera orientation as a variable, detecting camera rotation automatically and learning new decision criteria accordingly using a neural network model. We show that our proposed solution can detect WWD with a precision of 0.99 and a recall of 0.97. Due to its cheap computational cost and high error tolerance, our solution is easily scalable for statewide surveillance on a real-time basis to help decision-makers reduce fatalities due to WWD.