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Learning Algorithms for Anomaly Detection from Images

Learning Algorithms for Anomaly Detection from Images

Tarem Ahmed, Al-Sakib Khan Pathan, Supriyo Shafkat Ahmed
Copyright: © 2015 |Volume: 4 |Issue: 3 |Pages: 27
ISSN: 2160-9772|EISSN: 2160-9799|EISBN13: 9781466680371|DOI: 10.4018/IJSDA.2015070103
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MLA

Ahmed, Tarem, et al. "Learning Algorithms for Anomaly Detection from Images." IJSDA vol.4, no.3 2015: pp.43-69. http://doi.org/10.4018/IJSDA.2015070103

APA

Ahmed, T., Pathan, A. K., & Ahmed, S. S. (2015). Learning Algorithms for Anomaly Detection from Images. International Journal of System Dynamics Applications (IJSDA), 4(3), 43-69. http://doi.org/10.4018/IJSDA.2015070103

Chicago

Ahmed, Tarem, Al-Sakib Khan Pathan, and Supriyo Shafkat Ahmed. "Learning Algorithms for Anomaly Detection from Images," International Journal of System Dynamics Applications (IJSDA) 4, no.3: 43-69. http://doi.org/10.4018/IJSDA.2015070103

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Abstract

Visual surveillance networks are installed in many sensitive places in the present world. Human security officers are required to continuously stare at large numbers of monitors simultaneously, and for lengths of time at a stretch. Constant alert vigilance for hours on end is difficult to maintain for human beings. It is thus important to remove the onus of detecting unwanted activity from the human security officer to an automated system. While many researchers have proposed solutions to this problem in the recent past, significant gaps remain in existing knowledge. Most existing algorithms involve high complexities. No quantitative performance analysis is provided by most researchers. Most commercial systems require expensive equipment. This work proposes algorithms where the complexities are independent of time, making the algorithms naturally suited to online use. In addition, the proposed methods have been shown to work with the simplest surveillance systems that may already be publicly deployed. Furthermore, direct quantitative performance comparisons are provided.

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