Memorias de investigación
Artículos en revistas:
On-road visual vehicle tracking using Markov chain Monte Carlo with metropolis sampling
Año:2012
Áreas de investigación
  • Tecnología electrónica y de las comunicaciones
Datos
Descripción
In this study, a method for vehicle tracking through video analysis based on Markov chain Monte Carlo (MCMC) particle filtering with metropolis sampling is proposed. The method handles multiple targets with low computational requirements and is, therefore, ideally suited for advanced-driver assistance systems that involve real-time operation. The method exploits the removed perspective domain given by inverse perspective mapping (IPM) to define a fast and efficient likelihood model. Additionally, the method encompasses an interaction model using Markov Random Fields (MRF) that allows treatment of dependencies between the motions of targets. The proposed method is tested in highway sequences and compared to state-of-the-art methods for vehicle tracking, i.e., independent target tracking with Kalman filtering (KF) and joint tracking with particle filtering. The results showed fewer tracking failures using the proposed method.
Internacional
Si
JCR del ISI
Si
Título de la revista
Int. Journal of Automotive Technology
ISSN
1229-9138
Factor de impacto JCR
Información de impacto
Volumen
13
DOI
10.1007/s12239-012-0097-1
Número de revista
6
Desde la página
955
Hasta la página
961
Mes
OCTUBRE
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Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Tratamiento de Imágenes (GTI)