<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Garcia, Jorge</style></author><author><style face="normal" font="default" size="100%">Alfredo Gardel</style></author><author><style face="normal" font="default" size="100%">Ignacio Bravo</style></author><author><style face="normal" font="default" size="100%">Jose L. Lazaro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Tracking people motion base don extended condensation algorithm</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Systems, Man and Cybernetics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Extendend condensation algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">motion detection</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2013</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">www.ieeexplore.ieee.org</style></url></web-urls><related-urls><url><style face="normal" font="default" size="100%">https://geintra-uah.org/en/system/files/private/2013_smc_06425497.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE Xplore</style></publisher><pub-location><style face="normal" font="default" size="100%">EEUU</style></pub-location><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">606-618</style></pages><language><style face="normal" font="default" size="100%">English</style></language><abstract><style face="normal" font="default" size="100%">People counting systems are widely used in surveillance applications. In this paper, we present a solution to bidirectional people counting based on information provided by an overhead stereo system. Four fundamental aspects can be identified: the detection and tracking of human motion using an extended particle filter, the use of 3-D measurements in order to increase the system's robustness and a modified K-means algorithm to provide the number of hypotheses at each time, and, finally, trajectory generation to facilitate people counting in different directions. The proposed algorithm is designed to solve problems of occlusion, without counting objects such as shopping trolleys or bags. A processing ratio of around 30 frames/s is necessary in order to capture the real-time trajectory of people and obtain robust tracking results. We validated various test videos, achieving a hit rate between 95% and 99%, depending on the number of people crossing the counting area.

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