Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Video analysis based vehicle detection and tracking using an MCMC sampling framework
Año:2012
Áreas de investigación
  • Procesado y análisis de la señal
Datos
Descripción
This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is defined. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences.
Internacional
Si
JCR del ISI
Si
Título de la revista
EURASIP Journal on Advances in Signal Processing
ISSN
1687-6172
Factor de impacto JCR
1,053
Información de impacto
Volumen
2012
DOI
10.1186/1687-6180-2012-2
Número de revista
2
Desde la página
1
Hasta la página
17
Mes
ENERO
Ranking
118/247 (Q2) ENGINEERING, ELECTRICAL & ELECTRONIC
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Jon Arróspide Laborda (UPM)
  • Autor: Luis Salgado Alvarez de Sotomayor (UPM)
  • Autor: Marcos Nieto (Vicomtech-IK4, Research Alliance)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Señales, Sistemas y Radiocomunicaciones
  • Grupo de Investigación: Grupo de Tratamiento de Imágenes (GTI)
S2i 2022 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)