Visual tracking of multiple interacting objects through Rao-Blackwellized data association particle filtering

Blanco Adán, Carlos Roberto del; Jaureguizar Núñez, Fernando y García Santos, Narciso (2010). Visual tracking of multiple interacting objects through Rao-Blackwellized data association particle filtering. En: "17th IEEE International Conference on Image Processing (ICIP)", 26-29 de Septiembre del 2010, Hong Kong. ISBN 978-1-4244-7992-4.

Descripción

Título: Visual tracking of multiple interacting objects through Rao-Blackwellized data association particle filtering
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: 17th IEEE International Conference on Image Processing (ICIP)
Fechas del Evento: 26-29 de Septiembre del 2010
Lugar del Evento: Hong Kong
Título del Libro: 17th IEEE International Conference on Image Processing (ICIP)
Fecha: 3 Diciembre 2010
ISBN: 978-1-4244-7992-4
Materias:
Palabras Clave Informales: Atmospheric measurements , Clutter , Detectors , Particle filters , Particle measurements , Position measurement , Radar tracking
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

A multiple object visual tracking framework is presented, which is able to manage complex object interactions, missing detections and clutter. The main contribution is the ability to deal with complex situations in which the interacting objects can change their dynamics while they are occluded. This is achieved by explicitly estimating putative locations of the occluded objects. The tracking is modeled by a Rao-Blackwellized Data Association Particle Filter (RBDAPF), which has a tractable substructure that allows to analytically compute the object positions, while the object-measurement associations are approximated by Particle Filtering. Besides improving the accuracy, this filter decomposition reduces the computational cost, since the complexity with the number of objects becomes linear instead of exponential. The Particle Filter efficiently manages the measurements from visible and occluded objects, the clutter, and missing measurements to estimate the correct data associations that lead to a robust tracking. Experimental results on surveillance videos show that the proposed RBDAPF framework is able to track multiple interacting objects in complex situations.

Más información

ID de Registro: 7270
Identificador DC: http://oa.upm.es/7270/
Identificador OAI: oai:oa.upm.es:7270
URL Oficial: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5653411
Depositado por: Doctor Carlos Roberto del Blanco Adán
Depositado el: 27 May 2011 11:43
Ultima Modificación: 20 Abr 2016 16:27
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