A computer vision‐based deep learning model to detect wrong‐way driving using pan–tilt–zoom traffic cameras. (10th February 2022) - Haghighat, Arya; Sharma, Anuj
Abstract: 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.