<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Marta Marron</style></author><author><style face="normal" font="default" size="100%">Garcia, Juan Carlos</style></author><author><style face="normal" font="default" size="100%">Miguel Angel Sotelo</style></author><author><style face="normal" font="default" size="100%">Daniel Pizarro</style></author><author><style face="normal" font="default" size="100%">Ignacio Bravo</style></author><author><style face="normal" font="default" size="100%">Jose Luis Martin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Bayesian Solution to Robustly Track Multiple Objects from Visual Data</style></title><secondary-title><style face="normal" font="default" size="100%">INTELLIGENT TECHNIQUES AND TOOLS FOR NOVEL SYSTEM ARCHITECTURES</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Studies in Computational Intelligence</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">artificial vision</style></keyword><keyword><style  face="normal" font="default" size="100%">bayesian estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Multi-Object Tracking</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springer.com/engineering/mathematical/book/978-3-540-77621-5</style></url></web-urls><related-urls><url><style face="normal" font="default" size="100%">https://geintra-uah.org/system/files/private/a_bayesian_solution-fulltext.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">Springer-Verlag. </style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin/Heidelberg (ALEMANIA)</style></pub-location><volume><style face="normal" font="default" size="100%">109</style></volume><pages><style face="normal" font="default" size="100%">531-547</style></pages><isbn><style face="normal" font="default" size="100%">978-3-540-77621-5</style></isbn><language><style face="normal" font="default" size="100%">English</style></language><abstract><style face="normal" font="default" size="100%">Different solutions have been proposed for multiple objects tracking based on probabilistic algorithms. In this chapter, the authors propose the use of a single particle filter to track a variable number of objects in a complex environment.
Estimator robustness and adaptability are both increased by the use of a clustering algorithm. Measurements used in the tracking process are extracted from a stereovision system, and thus, the 3D position of the tracked objects is obtained at each time step. As a proof of concept, real results are obtained in a long sequence with a mobile robot moving in a cluttered scene.</style></abstract></record></records></xml>