Estimating surrounding vehicles' pose using computer vision

This paper presents a computer vision-based approach to tracking surrounding vehicles and estimating their trajectories, in order to detect potentially dangerous situations. Images are acquired using a camera mounted in the egovehicle. Estimations of the distance, velocity and orientation of other vehicles on the road are obtained by detecting their lights and shadow. Because 3D information is not readily available in a mono-camera system, several sets of constraints and assumptions on the geometry of both road and vehicles are proposed and tested in this paper. Kalman filters are used to track the detected vehicles. We also study the advantages of tracking the vehicles in road space (world coordinates), or tracking the position of the lights and shadows on the image. The performance of the approaches is evaluated on video recorded in urban environment.