Automated visual surveillance using kernel tricks

Automated visual surveillance using kernel tricks

Extensive network of multimodal surveillance and security sensors prevalent in many places. Task of simultaneously monitoring multiple images tedious and monotonous for a human. Existing algorithms involve high complexities, need significant memory and storage resources, and typically involve...

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Bibliographic Details
Main Authors: Ahmed, Tarem, Wei, Xianglin, Ahmed, Supriyo, Pathan, Al-Sakib Khan
Format: Conference or Workshop Item
Language:English
English
Published: 2013
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://irep.iium.edu.my/31259/
http://irep.iium.edu.my/31259/
http://irep.iium.edu.my/31259/1/CVML13_poster.pdf
http://irep.iium.edu.my/31259/4/lettre-confirmation-poster-TAREM.pdf
id iium-31259
recordtype eprints
spelling iium-312592013-08-20T04:05:18Z http://irep.iium.edu.my/31259/ Automated visual surveillance using kernel tricks Ahmed, Tarem Wei, Xianglin Ahmed, Supriyo Pathan, Al-Sakib Khan QA75 Electronic computers. Computer science Extensive network of multimodal surveillance and security sensors prevalent in many places. Task of simultaneously monitoring multiple images tedious and monotonous for a human. Existing algorithms involve high complexities, need significant memory and storage resources, and typically involve custom equipment. We present three algorithms built using kernel machines to perform automated, real-time intruder detection in surveillance systems. Proposed algorithms are adaptive and portable, with computational, storage and memory complexities independent of time, making them naturally suited to online use. 2013 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/31259/1/CVML13_poster.pdf application/pdf en http://irep.iium.edu.my/31259/4/lettre-confirmation-poster-TAREM.pdf Ahmed, Tarem and Wei, Xianglin and Ahmed, Supriyo and Pathan, Al-Sakib Khan (2013) Automated visual surveillance using kernel tricks. In: ENS/INRIA Visual Recognition and Machine Learning (CVML 2013), Summer School, 22-26 July, 2013, Paris, France. http://www.di.ens.fr/willow/events/cvml2013/CVML2013Posters.pdf
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ahmed, Tarem
Wei, Xianglin
Ahmed, Supriyo
Pathan, Al-Sakib Khan
Automated visual surveillance using kernel tricks
description Extensive network of multimodal surveillance and security sensors prevalent in many places. Task of simultaneously monitoring multiple images tedious and monotonous for a human. Existing algorithms involve high complexities, need significant memory and storage resources, and typically involve custom equipment. We present three algorithms built using kernel machines to perform automated, real-time intruder detection in surveillance systems. Proposed algorithms are adaptive and portable, with computational, storage and memory complexities independent of time, making them naturally suited to online use.
format Conference or Workshop Item
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 visual surveillance using kernel tricks
title_short Automated visual surveillance using kernel tricks
title_full Automated visual surveillance using kernel tricks
title_fullStr Automated visual surveillance using kernel tricks
title_full_unstemmed Automated visual surveillance using kernel tricks
title_sort automated visual surveillance using kernel tricks
publishDate 2013
url http://irep.iium.edu.my/31259/
http://irep.iium.edu.my/31259/
http://irep.iium.edu.my/31259/1/CVML13_poster.pdf
http://irep.iium.edu.my/31259/4/lettre-confirmation-poster-TAREM.pdf
first_indexed 2023-09-18T20:45:30Z
last_indexed 2023-09-18T20:45:30Z
_version_ 1777409647630614528

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