Detection of vehicle wheels from images using a pseudo-wavelet filter for analysis of congested traffic

There is potential for significant savings if the safety of existing bridges can be more accurately assessed. For longspan bridges, congestion is the governing traffic load condition. The current methods of simulating congestion make assumptions about the axle-to-axle gaps maintained between vehicles. There is potential for improvement in congestion models if accurate data on axle-to-axle gaps can be obtained. In this study, the use of a camera to collect this information is put forward. A new image processing technique is proposed to detect wheels in variable light conditions. The method is based on a pseudowavelet filter that amplifies circles, in conjunction with an algorithm that weights features in the image according to their circularity. This new approach is compared with the Hough transform, template matching and the deformable part-based model (DPM) methods previously developed. In a sample set of 80 images, 96.9% of wheels are detected, considerably more than with the Hough transform and template matching methods. It also provides the same level of accuracy as DPM without requiring a training process.