Video analysis based vehicle detection and tracking using an MCMC sampling framework

Arróspide Laborda, Jon and Salgado Álvarez de Sotomayor, Luis and Nieto Doncel, Marcos (2012). Video analysis based vehicle detection and tracking using an MCMC sampling framework. "EURASIP Journal on Advances in Signal Processing", v. 2012 (n. 2); pp. 1-17. ISSN 1687-6172. https://doi.org/10.1186/1687-6180-2012-2.

Description

Title: Video analysis based vehicle detection and tracking using an MCMC sampling framework
Author/s:
  • Arróspide Laborda, Jon
  • Salgado Álvarez de Sotomayor, Luis
  • Nieto Doncel, Marcos
Item Type: Article
Título de Revista/Publicación: EURASIP Journal on Advances in Signal Processing
Date: January 2012
Volume: 2012
Subjects:
Freetext Keywords: Object tracking; Monte Carlo methods; intelligent vehicles; HOG.
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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 dened. 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.

More information

Item ID: 10757
DC Identifier: http://oa.upm.es/10757/
OAI Identifier: oai:oa.upm.es:10757
DOI: 10.1186/1687-6180-2012-2
Official URL: http://asp.eurasipjournals.com/content/2012/1/2/abstract
Deposited by: Memoria Investigacion
Deposited on: 30 Apr 2012 09:55
Last Modified: 20 Apr 2016 18:59
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