LAUSR

In this paper, we propose a nonintrusive video-based system for vehicle speed measurement in urban roadways. Our system uses an optimized motion detector and a novel text detector to efficiently locate vehicle license plates in image regions containing motion. Distinctive features are then selected on the license plate regions, tracked across multiple frames, and rectified for perspective distortion. Vehicle speed is measured by comparing the trajectories of the tracked features to known real-world measures. The proposed system was tested on a data set containing approximately 5 h of videos recorded in different weather conditions by a single low-cost camera, with associated ground truth speeds obtained by an inductive loop detector. Our data set is freely available for research purposes. The measured speeds have an average error of −0.5 km/h, staying inside the [−3, +2] km/h limit determined by regulatory authorities in several countries in over 96.0% of the cases. To the authors' knowledge, there are no other video-based systems able to achieve results comparable to those produced by an inductive loop detector. We also show that our license plate detector outperforms two other published state-of-the-art text detectors, as well as a well-known license plate detector, achieving a precision of 0.93 and a recall of 0.87.