Post-processing approaches for improving people detection performance

This is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this docu...

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Authors: García Martín, Álvaro, Martínez Sánchez, José María
Format: article
Publication Date:2015
Country:España
Institution:Universidad Autónoma de Madrid
Repository:Biblos-e Archivo. Repositorio Institucional de la UAM
Language:English
OAI Identifier:oai:repositorio.uam.es:10486/666726
Online Access:http://hdl.handle.net/10486/666726
https://dx.doi.org/10.1016/j.cviu.2014.09.010
Access Level:Open access
Keyword:Decision-level fusion
Fusion methods
People detection
People-background segmentation
Segmentation confidence map
Segmentation mask
Telecomunicaciones
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spelling Post-processing approaches for improving people detection performanceGarcía Martín, ÁlvaroMartínez Sánchez, José MaríaDecision-level fusionFusion methodsPeople detectionPeople-background segmentationSegmentation confidence mapSegmentation maskTelecomunicacionesThis is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Vision and Image Understanding, 133 (2015) DOI: 10.1016/j.cviu.2014.09.010People detection in video surveillance environments is a task that has been generating great interest. There are many approaches trying to solve the problem either in controlled scenarios or in very specific surveillance applications. We address one of the main problems of people detection in video sequences: every people detector from the state of the art must maintain a balance between the number of false detections and the number of missing pedestrians. This compromise limits the global detection results. In order to reduce or relax this limitation and improve the detection results, we evaluate two different post-processing subtasks. Firstly, we propose the use of people-background segmentation as a filtering stage in people detection. Then, we evaluate the combination of different detection approaches in order to add robustness to the detection and therefore improve the detection results. And, finally, we evaluate the successive application of both post-processing approaches. Experiments have been performed on two extensive datasets and using different people detectors from the state of the art: the results show the benefits achieved using the proposed post-processing techniques.This work has been partially supported by the Spanish Government (TEC2011-25995 EventVideo).Elsevier B.V.Departamento de Tecnología Electrónica y de las ComunicacionesEscuela Politécnica SuperiorTratamiento e Interpretación de Vídeo (ING EPS-006)20152015-04-01research articlehttp://purl.org/coar/resource_type/c_2df8fbb1AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/666726https://dx.doi.org/10.1016/j.cviu.2014.09.010reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/6667262026-06-23T12:46:27Z
dc.title.none.fl_str_mv Post-processing approaches for improving people detection performance
title Post-processing approaches for improving people detection performance
spellingShingle Post-processing approaches for improving people detection performance
García Martín, Álvaro
Decision-level fusion
Fusion methods
People detection
People-background segmentation
Segmentation confidence map
Segmentation mask
Telecomunicaciones
title_short Post-processing approaches for improving people detection performance
title_full Post-processing approaches for improving people detection performance
title_fullStr Post-processing approaches for improving people detection performance
title_full_unstemmed Post-processing approaches for improving people detection performance
title_sort Post-processing approaches for improving people detection performance
dc.creator.none.fl_str_mv García Martín, Álvaro
Martínez Sánchez, José María
author García Martín, Álvaro
author_facet García Martín, Álvaro
Martínez Sánchez, José María
author_role author
author2 Martínez Sánchez, José María
author2_role author
dc.contributor.none.fl_str_mv Departamento de Tecnología Electrónica y de las Comunicaciones
Escuela Politécnica Superior
Tratamiento e Interpretación de Vídeo (ING EPS-006)
dc.subject.none.fl_str_mv Decision-level fusion
Fusion methods
People detection
People-background segmentation
Segmentation confidence map
Segmentation mask
Telecomunicaciones
topic Decision-level fusion
Fusion methods
People detection
People-background segmentation
Segmentation confidence map
Segmentation mask
Telecomunicaciones
description This is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Vision and Image Understanding, 133 (2015) DOI: 10.1016/j.cviu.2014.09.010
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-04-01
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/666726
https://dx.doi.org/10.1016/j.cviu.2014.09.010
url http://hdl.handle.net/10486/666726
https://dx.doi.org/10.1016/j.cviu.2014.09.010
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,223283

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