On Crowd Density Estimation for Surveillance

The goal of this work is to use computer vision to measure crowd density in outdoor scenes. Crowd density estimation is an important task in crowd monitoring. The assessment is carried out using images of a graduation scene which illustrated variation of illumination due to textured brick surface, c...

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Main Author: Rahmalan, H.
Format: Conference or Workshop Item
Language:English
Published: 2006
Subjects:
Online Access:http://eprints.utem.edu.my/id/eprint/94/
http://eprints.utem.edu.my/id/eprint/94/1/on_Crowd_Density_Estimation_for_Surveillance_20006.pdf
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author Rahmalan, H.
author_facet Rahmalan, H.
author_sort Rahmalan, H.
building UTeM Institutional Repository
collection Online Access
description The goal of this work is to use computer vision to measure crowd density in outdoor scenes. Crowd density estimation is an important task in crowd monitoring. The assessment is carried out using images of a graduation scene which illustrated variation of illumination due to textured brick surface, clothing and changes of weather. Image features were extracted using grey level dependency matrix, Minkowski fractal dimension and a new method called translation invariant orthonormal Chebyshev moments. The features were then classified into a range of density by using a self organizing map. Three different techniques were used and a comparison on the classification results investigates the best performance for measuring crowd density by vision
first_indexed 2025-11-15T19:45:36Z
format Conference or Workshop Item
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institution Universiti Teknikal Malaysia Melaka
institution_category Local University
language English
last_indexed 2025-11-15T19:45:36Z
publishDate 2006
recordtype eprints
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spelling utem-942015-05-28T02:16:43Z http://eprints.utem.edu.my/id/eprint/94/ On Crowd Density Estimation for Surveillance Rahmalan, H. Q Science (General) The goal of this work is to use computer vision to measure crowd density in outdoor scenes. Crowd density estimation is an important task in crowd monitoring. The assessment is carried out using images of a graduation scene which illustrated variation of illumination due to textured brick surface, clothing and changes of weather. Image features were extracted using grey level dependency matrix, Minkowski fractal dimension and a new method called translation invariant orthonormal Chebyshev moments. The features were then classified into a range of density by using a self organizing map. Three different techniques were used and a comparison on the classification results investigates the best performance for measuring crowd density by vision 2006-06 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/94/1/on_Crowd_Density_Estimation_for_Surveillance_20006.pdf Rahmalan, H. (2006) On Crowd Density Estimation for Surveillance. In: The Institution of Engineering and Technology Conference on Crime and Security , 2006.
spellingShingle Q Science (General)
Rahmalan, H.
On Crowd Density Estimation for Surveillance
title On Crowd Density Estimation for Surveillance
title_full On Crowd Density Estimation for Surveillance
title_fullStr On Crowd Density Estimation for Surveillance
title_full_unstemmed On Crowd Density Estimation for Surveillance
title_short On Crowd Density Estimation for Surveillance
title_sort on crowd density estimation for surveillance
topic Q Science (General)
url http://eprints.utem.edu.my/id/eprint/94/
http://eprints.utem.edu.my/id/eprint/94/1/on_Crowd_Density_Estimation_for_Surveillance_20006.pdf