<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mikael Lindeborg</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Marta Marron</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Tracking Multiple Objects with Kalman Filters, part II</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Erasmus</style></keyword><keyword><style  face="normal" font="default" size="100%">Kalman filters</style></keyword><keyword><style  face="normal" font="default" size="100%">Multi-Object Tracking</style></keyword><keyword><style  face="normal" font="default" size="100%">Real-time implementation</style></keyword><keyword><style  face="normal" font="default" size="100%">TFC</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2006</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://geintra-uah.org/en/system/files/Thesis_-_Micke.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">Department of Electronics</style></publisher><pub-location><style face="normal" font="default" size="100%">Alcala de Henares (SPAIN)</style></pub-location><pages><style face="normal" font="default" size="100%">68</style></pages><language><style face="normal" font="default" size="100%">English</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;font face=&quot;Arial&quot;&gt;
&lt;p align=&quot;left&quot;&gt;In this report the implementation of a multiple object tracking algorithm is described. The algorithm is part of the obstacle avoidance system in an autonomous robot. The measurement vector used to achieve the tracking task comes from a stereo-vision system that detects objects in the robot&amp;rsquo;s environment [1]. The algorithm uses the probabilistic Kalman filter (KF) to estimate the position and movement of different objects in the scene. One filter is used for each object to track. An algorithm for associating the data in the measurement vector to different objects is described. A validation process that the tracking algorithm uses to reduce the noise included in the measurement vector is also described.&lt;/p&gt;
&lt;/font&gt;&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Master</style></work-type><custom1><style face="normal" font="default" size="100%">Master in Electronics</style></custom1><custom2><style face="normal" font="default" size="100%">Escuela Politecnica Superior</style></custom2><custom3><style face="normal" font="default" size="100%">University of Alcala</style></custom3><num-vols><style face="normal" font="default" size="100%">1 vol.</style></num-vols></record></records></xml>