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On Crowd Density Estimation for Surveillance

Rahmalan, H. (2006) On Crowd Density Estimation for Surveillance. In: The Institution of Engineering and Technology Conference on Crime and Security , 2006.

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Abstract

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

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Minkowski fractal dimension;computer vision;crowd density estimation;crowd monitoring;feature classification;grey level dependency matrix;image features extraction;outdoor scenes;self organizing map;translation invariant orthonormal Chebyshev moments;video surveillance;Chebyshev approximation;computer vision;feature extraction;image classification;matrix algebra;video surveillance;
Subjects: Q Science > Q Science (General)
Divisions: Faculty of Information and Communication Technology > Department of Software Engineeering
Depositing User: Hidayah Rahmalan
Date Deposited: 08 Aug 2011 04:16
Last Modified: 28 May 2015 02:16
URI: http://eprints.utem.edu.my/id/eprint/94

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