Fast heuristic method to detect people in frontal depth images
Authors
Luna Vázquez, Carlos Andrés; Losada Gutiérrez, Cristina; Fuentes Jiménez, David; Mazo Quintas, Manuel RamónIdentifiers
Permanent link (URI): http://hdl.handle.net/10017/59874DOI: 10.1016/j.eswa.2020.114483
ISSN: 0957-4174
Publisher
Elsevier
Date
2021-04-15Funders
Ministerio de Economía y Competitivdad
Universidad de Alcalá
Bibliographic citation
Carlos A. Luna, Cristina Losada-Gutiérrez, David Fuentes-Jiménez, Manuel Mazo, Fast heuristic method to detect people in frontal depth images, Expert Systems with Applications, Volume 168, 2021, 114483.
Keywords
3D People detection
Depth camera
Frontal Depth images
Feature extraction
Head biometric classification
Project
info:eu-repo/grantAgreement/MINECO//TIN2016-75982-C2-1-R/ES/DETECCION SEMANTICA MULTISENSORIAL DE SITUACIONES ANOMALAS EN ENTORNOS SIN RESTRICCIONES/
info:eu-repo/grantAgreement/UAH//CCG2018%2FEXP-029
info:eu-repo/grantAgreement/UAH//CCG2019%2FIA-024
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/submittedVersion
Publisher's version
https://doi.org/10.1016/j.eswa.2020.114483Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2020 Elsevier
Access rights
info:eu-repo/semantics/openAccess
Abstract
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%.
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