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

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

Description

Title: Visual tracking of multiple interacting objects through Rao-Blackwellized data association particle filtering
Author/s:
  • Blanco Adán, Carlos Roberto del
  • Jaureguizar Núñez, Fernando
  • García Santos, Narciso
Item Type: Presentation at Congress or Conference (Article)
Event Title: 17th IEEE International Conference on Image Processing (ICIP)
Event Dates: 26-29 de Septiembre del 2010
Event Location: Hong Kong
Title of Book: 17th IEEE International Conference on Image Processing (ICIP)
Date: 3 December 2010
ISBN: 978-1-4244-7992-4
Subjects:
Freetext Keywords: Atmospheric measurements , Clutter , Detectors , Particle filters , Particle measurements , Position measurement , Radar tracking
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

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.

More information

Item ID: 7270
DC Identifier: http://oa.upm.es/7270/
OAI Identifier: oai:oa.upm.es:7270
Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5653411
Deposited by: Doctor Carlos Roberto del Blanco Adán
Deposited on: 27 May 2011 11:43
Last Modified: 20 Apr 2016 16:27
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