Learning algorithms for anomaly detection from images
This book chapter was published in the IGI Global [© 2017 IGI Global] and the definite version is available at : http://doi.org/10.4018/978-1-5225-0983-7.ch013 The Journal's website is at: https://www.igi-global.com/gateway/chapter/164608
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| Acesso em linha: | http://hdl.handle.net/10361/9876 http://doi.org/10.4018/978-1-5225-0983-7.ch013 |
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10361-98762018-04-15T09:56:41Z Learning algorithms for anomaly detection from images Ahmed, Tarem Pathan, Al Sakib Khan Ahmed, Supriyo Shafkat Department of Electrical and Electronic Engineering, BRAC University Anomaly detection Automated systems Commercial systems Expensive equipments Performance analysis Visual surveillance Automation This book chapter was published in the IGI Global [© 2017 IGI Global] and the definite version is available at : http://doi.org/10.4018/978-1-5225-0983-7.ch013 The Journal's website is at: https://www.igi-global.com/gateway/chapter/164608 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. Published 2018-04-15T09:55:08Z 2018-04-15T09:55:08Z 2016-08-30 Book chapter Ahmed, T., Pathan, A. -. K., & Ahmed, S. S. (2016). Learning algorithms for anomaly detection from images. Biometrics: Concepts, methodologies, tools, and applications (pp. 281-308) doi:10.4018/978-1-5225-0983-7.ch013 978-152250984-4 978-152250983-7 http://hdl.handle.net/10361/9876 http://doi.org/10.4018/978-1-5225-0983-7.ch013 en https://www.igi-global.com/gateway/chapter/164608 © 2017 IGI Global |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Anomaly detection Automated systems Commercial systems Expensive equipments Performance analysis Visual surveillance Automation |
| spellingShingle |
Anomaly detection Automated systems Commercial systems Expensive equipments Performance analysis Visual surveillance Automation Ahmed, Tarem Pathan, Al Sakib Khan Ahmed, Supriyo Shafkat Learning algorithms for anomaly detection from images |
| description |
This book chapter was published in the IGI Global [© 2017 IGI Global] and the definite version is available at : http://doi.org/10.4018/978-1-5225-0983-7.ch013 The Journal's website is at: https://www.igi-global.com/gateway/chapter/164608 |
| author2 |
Department of Electrical and Electronic Engineering, BRAC University |
| author_facet |
Department of Electrical and Electronic Engineering, BRAC University Ahmed, Tarem Pathan, Al Sakib Khan Ahmed, Supriyo Shafkat |
| format |
Book chapter |
| author |
Ahmed, Tarem Pathan, Al Sakib Khan Ahmed, Supriyo Shafkat |
| author_sort |
Ahmed, Tarem |
| title |
Learning algorithms for anomaly detection from images |
| title_short |
Learning algorithms for anomaly detection from images |
| title_full |
Learning algorithms for anomaly detection from images |
| title_fullStr |
Learning algorithms for anomaly detection from images |
| title_full_unstemmed |
Learning algorithms for anomaly detection from images |
| title_sort |
learning algorithms for anomaly detection from images |
| publisher |
© 2017 IGI Global |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/9876 http://doi.org/10.4018/978-1-5225-0983-7.ch013 |
| work_keys_str_mv |
AT ahmedtarem learningalgorithmsforanomalydetectionfromimages AT pathanalsakibkhan learningalgorithmsforanomalydetectionfromimages AT ahmedsupriyoshafkat learningalgorithmsforanomalydetectionfromimages |
| _version_ |
1814309343239077888 |