Automated visual surveillance using kernel tricks

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.

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Abstract

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.

Item Type:	Conference or Workshop Item (Poster)
Additional Information:	6481/31259 ---This work was partially supported by the Research Endowment Grant (Type B), Project Title: ‘Automated Intruder Detection in Surveillance Networks using Machine Learning Algorithms’ (grant number EDW B11-167-0645) at the International Islamic University Malaysia, Kuala Lumpur, Malaysia, for which Al-Sakib Khan Pathan is the Principal Researcher. Tarem Ahmed (Ph.D. student) presented the POSTER at the event.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr. Al-Sakib Khan Pathan
Date Deposited:	20 Aug 2013 11:53
Last Modified:	20 Aug 2013 12:05
URI:	http://irep.iium.edu.my/id/eprint/31259

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