Vehicle detection and tracking using homography-based plane rectification and particle filtering

This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most remarkably, a novel probabilistic framework based on Kalman filtering is presented for reliable and accurate homography estimation. The estimated homography is used for image alignment, which in turn allows to detect the moving vehicles in the image. Tracking of vehicles is performed on the basis of a multidimensional particle filter, which also manages the exit and entries of objects. The filter involves a mixture likelihood model that allows a better adaptation of the particles to the observed measurements. The system is specially designed for highway environments, where it has been proven to yield excellent results

Vehicle Dynamics Estimation for Camera-based Visibility Distance Estimation

The presence of an area with low visibility conditions is a relevant information for autonomous vehicle as far as environment sensing is important. In this aim, we develop a generic sensor of visibility using an onboard camera in a vehicle. Our approach consists in estimating the range to the most distant object belonging to the plane of the road having at least 5% of contrast. The originality of this approach lies in the fact that the depth map of the vehicle environment is obtained by aligning the road plane in successive images. This algorithm exploits the dynamics of the vehicle which is given or observed by proprioceptive sensors. In this paper, we present the principle of our approach in terms of image processing and explain how the vehicle dynamics takes part in it with a sensitivity study