<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fernandez-Rincon, Alvaro</style></author><author><style face="normal" font="default" size="100%">Fuentes-Jimenez, David</style></author><author><style face="normal" font="default" size="100%">Cristina Losada-Gutierrez</style></author><author><style face="normal" font="default" size="100%">Marta Marron-Romera</style></author><author><style face="normal" font="default" size="100%">Luna, Carlos A.</style></author><author><style face="normal" font="default" size="100%">Javier Macias-Guarasa</style></author><author><style face="normal" font="default" size="100%">Manuel Mazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Robust People Detection and Tracking from an overhead Time-of-Flight Camera</style></title><secondary-title><style face="normal" font="default" size="100%">12th International Conference on Computer Vision Theory and Applications.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2017</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Porto, Portugal</style></pub-location><pages><style face="normal" font="default" size="100%">556-564</style></pages><isbn><style face="normal" font="default" size="100%">978-989-758-225-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we describe a system for robust detection of people in a scene, by using an overhead Time of Flight (ToF) camera. The proposal addresses the problem of robust detection of people, by three means: a carefully designed algorithm to select regions of interest as candidates to belong to people; the generation of a robust feature vector that efficiently model the human upper body; and a people classification stage, to allow robust discrimination of people and other objects in the scene. The proposal also includes a particle filter tracker to allow people identification and tracking. Two classifiers are evaluated, based on Principal Component Analysis (PCA), and Support Vector Machines (SVM). The evaluation is carried out on a subset of a carefully designed dataset with a broad variety of conditions, providing results comparing the PCA and SVM approaches, and also the performance impact of the tracker, with satisfactory results.</style></abstract></record></records></xml>