Open Access

Vehicle Trajectory Estimation Using Spatio-Temporal MCMC

EURASIP Journal on Advances in Signal Processing20102010:712854

Received: 19 October 2009

Accepted: 23 March 2010

Published: 3 May 2010


This paper presents an algorithm for modeling and tracking vehicles in video sequences within one integrated framework. Most of the solutions are based on sequential methods that make inference according to current information. In contrast, we propose a deferred logical inference method that makes a decision according to a sequence of observations, thus processing a spatio-temporal search on the whole trajectory. One of the drawbacks of deferred logical inference methods is that the solution space of hypotheses grows exponentially related to the depth of observation. Our approach takes into account both the kinematic model of the vehicle and a driver behavior model in order to reduce the space of the solutions. The resulting proposed state model explains the trajectory with only 11 parameters. The solution space is then sampled with a Markov Chain Monte Carlo (MCMC) that uses a model-driven proposal distribution in order to control random walk behavior. We demonstrate our method on real video sequences from which we have ground truth provided by a RTK GPS (Real-Time Kinematic GPS). Experimental results show that the proposed algorithm outperforms a sequential inference solution (particle filter).


Markov Chain Monte CarloVideo SequenceProposal DistributionDriver BehaviorReal Video

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Authors’ Affiliations

LCPC, Bouguenais, France
LASMEA, Université Blaise Pascal, Aubière, France


© Yann Goyat et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.