loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Thierry Chesnais 1 ; Thierry Chateau 2 ; Nicolas Allezard 1 ; Yoann Dhome 1 ; Boris Meden 1 ; Mohamed Tamaazousti 1 and Adrien Chan-Hon-Tong 1

Affiliations: 1 CEA, LIST and Vision and Content Engineering Laboratory, France ; 2 UMR 6602 CNRS and Blaise Pascal University, France

Keyword(s): Videosurveillance, Object Detection, Pedestrian Detection, Semi-supervised Learning, Oracle.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Motion, Tracking and Stereo Vision ; Video Surveillance and Event Detection

Abstract: This paper tackles the real-time pedestrian detection problem using a stationary calibrated camera. Problems frequently encountered are: a generic classifier can not be adjusted to each situation and the perspective deformations of the camera can profoundly change the appearance of a person. To avoid these drawbacks we contextualized a detector with information coming directly from the scene. Our method comprises three distinct parts. First an oracle gathers examples from the scene. Then, the scene is split in different regions and one classifier is trained for each one. Finally each detector are automatically tuned to achieve the best performances. Designed for making camera network installation procedure easier, our method is completely automatic and does not need any knowledge about the scene.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.119.105.155

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Chesnais, T.; Chateau, T.; Allezard, N.; Dhome, Y.; Meden, B.; Tamaazousti, M. and Chan-Hon-Tong, A. (2013). A Region Driven and Contextualized Pedestrian Detector. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP; ISBN 978-989-8565-47-1; ISSN 2184-4321, SciTePress, pages 796-799. DOI: 10.5220/0004292607960799

@conference{visapp13,
author={Thierry Chesnais. and Thierry Chateau. and Nicolas Allezard. and Yoann Dhome. and Boris Meden. and Mohamed Tamaazousti. and Adrien Chan{-}Hon{-}Tong.},
title={A Region Driven and Contextualized Pedestrian Detector},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP},
year={2013},
pages={796-799},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004292607960799},
isbn={978-989-8565-47-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP
TI - A Region Driven and Contextualized Pedestrian Detector
SN - 978-989-8565-47-1
IS - 2184-4321
AU - Chesnais, T.
AU - Chateau, T.
AU - Allezard, N.
AU - Dhome, Y.
AU - Meden, B.
AU - Tamaazousti, M.
AU - Chan-Hon-Tong, A.
PY - 2013
SP - 796
EP - 799
DO - 10.5220/0004292607960799
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic