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 detection and tracking using homography-based plane rectification and particle filtering
2010 IEEE Intelligent Vehicles Symposium ; 150-155
01.06.2010
2028167 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
British Library Conference Proceedings | 2010
|SAE Technical Papers | 2013
|British Library Conference Proceedings | 2013
|Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography
ArXiv | 2021
|