Vehicle speed estimation using extracted SURF features from stereo images
Abderrahim El Bouziady, Rachid Oulad Haj Thami, Mounir Ghogho, Omar Bourja, Sanaa El Fkihi · 2018
In this paper, we present a novel technique to estimate vehicle speed on highway using stereo images. First, traffic images are captured using calibrated and synchronized stereo cameras, then we detect moving vehicles on the left image by subtracting the background image. On each detected vehicle, we extract and match Speed Up Robust Features (SURF) in order to compute sparse depth maps. Finally, we get vehicle speed from vehicle depth variation using some geometric derivations. The experiments shows that the proposed algorithm has a satisfactory estimation of vehicle speed comparing to GPS ground truth with a speed error of 2 Km/h in the Moroccan environment.