<?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%">Luna, Carlos A.</style></author><author><style face="normal" font="default" size="100%">Cristina Losada-Gutiérrez</style></author><author><style face="normal" font="default" size="100%">Fuentes-Jiménez, David</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%">Fast heuristic method to detect people in frontal depth images</style></title><secondary-title><style face="normal" font="default" size="100%">Expert Systems with Applications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">3D People detection</style></keyword><keyword><style  face="normal" font="default" size="100%">Depth camera</style></keyword><keyword><style  face="normal" font="default" size="100%">Feature extraction</style></keyword><keyword><style  face="normal" font="default" size="100%">Frontal Depth images</style></keyword><keyword><style  face="normal" font="default" size="100%">Head biometric classification</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S0957417420311301</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">168</style></volume><pages><style face="normal" font="default" size="100%">114483</style></pages><language><style face="normal" font="default" size="100%">English</style></language><abstract><style face="normal" font="default" size="100%">This paper presents a new method for detecting people using only depth images captured by a camera in a frontal position. The approach is based on first detecting all the objects present in the scene and determining their average depth (distance to the camera). Next, for each object, a 3D Region of Interest (ROI) is processed around it in order to determine if the characteristics of the object correspond to the biometric characteristics of a human head. The results obtained using three public datasets captured by three depth sensors with different spatial resolutions and different operation principle (structured light, active stereo vision and Time of Flight) are presented. These results demonstrate that our method can run in realtime using a low-cost CPU platform with a high accuracy, being the processing times smaller than 1 ms per frame for a 512 × 424 image resolution with a precision of 99.26% and smaller than 4 ms per frame for a 1280 × 720 image resolution with a precision of 99.77%.</style></abstract></record></records></xml>