Bayesian Visual Surveillance, a Model for Detecting and Tracking a variable number of moving objects

Blanco Adán, Carlos Roberto del; Jaureguizar Núñez, Fernando y García Santos, Narciso (2011). Bayesian Visual Surveillance, a Model for Detecting and Tracking a variable number of moving objects. En: "IEEE International Conference on Image Processing 2011", 13-14 de Enero del 2011, Bruselas, Bélgica. ISBN 978-1-4577-1304-0.

Descripción

Título: Bayesian Visual Surveillance, a Model for Detecting and Tracking a variable number of moving objects
Autor/es:
  • Blanco Adán, Carlos Roberto del
  • Jaureguizar Núñez, Fernando
  • García Santos, Narciso
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: IEEE International Conference on Image Processing 2011
Fechas del Evento: 13-14 de Enero del 2011
Lugar del Evento: Bruselas, Bélgica
Título del Libro: IEEE International Conference on Image Processing 2011
Fecha: 11 Septiembre 2011
ISBN: 978-1-4577-1304-0
Materias:
Palabras Clave Informales: Split detections, merged detections, moving regions, multiple object tracking, variable number of objects.
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Ninguna

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Resumen

An automatic detection and tracking framework for visual surveillance is proposed, which is able to handle a variable number of moving objects. Video object detectors generate an unordered set of noisy, false, missing, split, and merged measurements that make extremely complex the tracking task. Especially challenging are split detections (one object is split into several measurements) and merged detections (several objects are merged into one detection). Few approaches address this problem directly, and the existing ones use heuristics methods, or assume a known number of objects, or are not suitable for on-line applications. In this paper, a Bayesian Visual Surveillance Model is proposed that is able to manage undesirable measurements. Particularly, split and merged measurements are explicitly modeled by stochastic processes. Inference is accurately performed through a particle filtering approach that combines ancestral and MCMC sampling. Experimental results have shown a high performance of the proposed approach in real situations.

Más información

ID de Registro: 10365
Identificador DC: http://oa.upm.es/10365/
Identificador OAI: oai:oa.upm.es:10365
URL Oficial: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6115712
Depositado por: Doctor Carlos Roberto del Blanco Adán
Depositado el: 17 Feb 2012 09:01
Ultima Modificación: 20 Abr 2016 18:34
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