Vehicle Trajectory Estimation Using Spatio-Temporal MCMC - l'unam - université nantes angers le mans Accéder directement au contenu
Article Dans Une Revue EURASIP Journal on Advances in Signal Processing Année : 2010

Vehicle Trajectory Estimation Using Spatio-Temporal MCMC

Résumé

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).
Fichier principal
Vignette du fichier
doc00006109.pdf (1.75 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00587921 , version 1 (21-04-2011)

Identifiants

Citer

Yann Goyat, Thierry Chateau, François Bardet. Vehicle Trajectory Estimation Using Spatio-Temporal MCMC. EURASIP Journal on Advances in Signal Processing, 2010, 2010, 8p. ⟨10.1155/2010/712854⟩. ⟨hal-00587921⟩
111 Consultations
189 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More