Minimising Misclassifications of Over-Height Vehicles Due to Wind

Abstract

Over-height vehicle strikes with low bridges and tunnels are an ongoing problem worldwide. While previous methods have used vision-based systems to address the over-height warning problem, such methods are sensitive to wind. In this paper, we propose a constraint-based approach to minimise the number of over-height vehicle misclassifications due to windy conditions. The dataset includes a total of 102 over-height vehicles recorded at frame rates of 25 and 30 fps. At this frame rate, we analysed sampling rates to determine the sufficient number of positive frames required to provide accurate warnings to drivers. Optical flow and KLT feature-tracker algorithm was used to detect and track feature points of motion. Motion captured within the region of interest was treated as a standard two-class binary linear classification problem with 1 indicating over-height vehicle presence and 0 indicating noise. The algorithm performed with 100% recall, 83.3% precision and false positive rate of 8.3%.