Title :
MCMC Particle Filter for Real-Time Visual Tracking of Vehicles
Author :
Bardet, François ; Chateau, Thierry
Author_Institution :
LASMEA, Aubiere
Abstract :
This paper adresses real-time automatic tracking and labeling of a variable number of vehicles, using one or more still cameras. The multi-vehicle configuration is tracked through a Markov Chain Monte-Carlo Particle Filter (MCMC PF) method. We show that integrating a simple vehicle kinematic model within this tracker allows to estimate the trajectories of a set of vehicles, with a moderate number of particles, allowing frame-rate computation. This paper also adresses vehicle tracking involving occlusions, deep scale and appearance changes: we propose a global observation function allowing to fairly track far vehicles as well as close vehicles. Experiment results are shown and discussed on multiple vehicle tracking sequences. Though now only tracking light vehicles, the ultimate goal of this research is to track and classify all classes of road users, also including trucks, cycles and pedestrians, in order to analyze road users interactions.
Keywords :
Markov processes; Monte Carlo methods; automated highways; image segmentation; object detection; road traffic; tracking filters; video surveillance; Markov Chain Monte-Carlo particle filter; foreground segmentation; intelligent transportation system; real-time automatic vehicle tracking; real-time visual vehicle tracking; vehicle kinematic model; video traffic surveillance; Cameras; Intelligent sensors; Intelligent transportation systems; Particle filters; Particle tracking; Radar tracking; Real time systems; Roads; Surveillance; Vehicle dynamics;
Conference_Titel :
Intelligent Transportation Systems, 2008. ITSC 2008. 11th International IEEE Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2111-4
Electronic_ISBN :
978-1-4244-2112-1
DOI :
10.1109/ITSC.2008.4732627