Observatorio de I+D+i UPM

Descripción
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.
Internacional	Si
Nombre congreso	IEEE Intelligent Vehicles Symposium, IV 2010
Tipo de participación	960
Lugar del congreso	San Diego (CA), USA
Revisores	Si
ISBN o ISSN	978-1-4244-7866-8
DOI	10.1109/IVS.2010.5547980
Fecha inicio congreso	21/06/2010
Fecha fin congreso	24/06/2010
Desde la página	150
Hasta la página	155
Título de las actas	Proc. IEEE Intelligent Vehicles Symposium, IV 2010

Esta actividad pertenece a memorias de investigación

• Autor: Jon Arróspide Laborda (UPM)
• Autor: Luis Salgado Alvarez de Sotomayor (UPM)
• Autor: Marcos Nieto Doncel (UPM)

• Creador: Grupo de Investigación: Grupo de Tratamiento de Imágenes (GTI)
• Departamento: Señales, Sistemas y Radiocomunicaciones