Automated intruder detection from image sequences using minimum volume sets

Automated intruder detection from image sequences using minimum volume sets

We propose a new algorithm based on machine learning techniques for automatic intruder detection in visual surveillance networks. The proposed algorithm is theoretically founded on the concept of Minimum Volume Sets. Through application to image sequences from two different scenarios and compariso...

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Bibliographic Details
Main Authors: Ahmed, Tarem, Wei, Xianglin, Ahmed, Supriyo, Pathan, Al-Sakib Khan
Format: Article
Language:English
Published: Kohat University of Science and Technology (KUST), Pakistan 2012
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
Online Access:http://irep.iium.edu.my/22265/
http://irep.iium.edu.my/22265/
http://irep.iium.edu.my/22265/4/88-453-1-PB.pdf
id iium-22265
recordtype eprints
spelling iium-222652012-06-15T03:09:09Z http://irep.iium.edu.my/22265/ Automated intruder detection from image sequences using minimum volume sets Ahmed, Tarem Wei, Xianglin Ahmed, Supriyo Pathan, Al-Sakib Khan QA75 Electronic computers. Computer science QA76 Computer software We propose a new algorithm based on machine learning techniques for automatic intruder detection in visual surveillance networks. The proposed algorithm is theoretically founded on the concept of Minimum Volume Sets. Through application to image sequences from two different scenarios and comparison with existing algorithms, we show that it is possible for our proposed algorithm to easily obtain high detection accuracy with low false alarm rates. Kohat University of Science and Technology (KUST), Pakistan 2012-04 Article PeerReviewed application/pdf en http://irep.iium.edu.my/22265/4/88-453-1-PB.pdf Ahmed, Tarem and Wei, Xianglin and Ahmed, Supriyo and Pathan, Al-Sakib Khan (2012) Automated intruder detection from image sequences using minimum volume sets. International Journal of Communication Networks and Information Security, 4 (1). pp. 11-17. ISSN 2073-607X (O), 2076-0930 (P) http://www.ijcnis.org/index.php/ijcnis/article/view/88
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic QA75 Electronic computers. Computer science
QA76 Computer software
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
Ahmed, Tarem
Wei, Xianglin
Ahmed, Supriyo
Pathan, Al-Sakib Khan
Automated intruder detection from image sequences using minimum volume sets
description We propose a new algorithm based on machine learning techniques for automatic intruder detection in visual surveillance networks. The proposed algorithm is theoretically founded on the concept of Minimum Volume Sets. Through application to image sequences from two different scenarios and comparison with existing algorithms, we show that it is possible for our proposed algorithm to easily obtain high detection accuracy with low false alarm rates.
format Article
author Ahmed, Tarem
Wei, Xianglin
Ahmed, Supriyo
Pathan, Al-Sakib Khan
author_facet Ahmed, Tarem
Wei, Xianglin
Ahmed, Supriyo
Pathan, Al-Sakib Khan
author_sort Ahmed, Tarem
title Automated intruder detection from image sequences using minimum volume sets
title_short Automated intruder detection from image sequences using minimum volume sets
title_full Automated intruder detection from image sequences using minimum volume sets
title_fullStr Automated intruder detection from image sequences using minimum volume sets
title_full_unstemmed Automated intruder detection from image sequences using minimum volume sets
title_sort automated intruder detection from image sequences using minimum volume sets
publisher Kohat University of Science and Technology (KUST), Pakistan
publishDate 2012
url http://irep.iium.edu.my/22265/
http://irep.iium.edu.my/22265/
http://irep.iium.edu.my/22265/4/88-453-1-PB.pdf
first_indexed 2023-09-18T20:34:00Z
last_indexed 2023-09-18T20:34:00Z
_version_ 1777408923448377344

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