Tarem Ahmed
http://hdl.handle.net/10361/6901
2024-03-28T13:01:56Z
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Learning algorithms for anomaly detection from images
http://hdl.handle.net/10361/9876
Learning algorithms for anomaly detection from images
Ahmed, Tarem; Pathan, Al Sakib Khan; Ahmed, Supriyo Shafkat
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.
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
2016-08-30T00:00:00Z
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Automated intruder detection from image sequences using minimum volume sets
http://hdl.handle.net/10361/7206
Automated intruder detection from image sequences using minimum volume sets
Ahmed, Tarem; Wei, Xianglin; Ahmed, Supriyo Sabbir; Pathan, Al-Sakib Khan
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.
This article was published in the International Journal of Communication Networks and Information Security [© 2014 IJCNIS] and The Article's website is at:
http://www.ijcnis.org/index.php/ijcnis/article/view/88
2012-01-01T00:00:00Z
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Efficient and effective automated surveillance agents using kernel tricks
http://hdl.handle.net/10361/7204
Efficient and effective automated surveillance agents using kernel tricks
Ahmed, Tarem; Pathan, Al-Sakib Khan; Ahmed, Supriyo Sabbir; Wei, Xianglin
Many schemes have been presented over the years to develop automated visual surveillance systems. However, these schemes typically need custom equipment, or involve significant complexity and storage requirements. In this paper we present three software-based agents built using kernel machines to perform automated, real-time intruder detection in surveillance systems. Kernel machines provide a powerful data mining technique that may be used for pattern matching in the presence of complex data. They work by first mapping the raw input data onto a (often much) higher-dimensional feature space, and then clustering in the feature space instead. The reasoning is that mapping onto the (higher-dimensional) feature space enables the comparison of additional, higher-order correlations in determining patterns between the raw data points. The agents proposed here have been built using algorithms that are adaptive, portable, do not require any expensive or sophisticated components, and are lightweight and efficient having run times of the order of hundredths of a second. Through application to real image streams from a simple, run-of-the-mill closed-circuit television surveillance system, and direct quantitative performance comparison with some existing schemes, we show that it is possible to easily obtain high detection accuracy with low computational and storage complexities.
This article was published in the SIMULATION [© 2012 The Society for Modeling and Simulation International.] and the definite version is available at: http://doi.org/10.1177/0037549712460908 The Article's website is at: http://sim.sagepub.com/content/89/5/562
2012-01-01T00:00:00Z
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Adaptive algorithms for automated intruder detection in surveillance networks
http://hdl.handle.net/10361/7033
Adaptive algorithms for automated intruder detection in surveillance networks
Ahmed, Tarem; Ahmed, Supriyo Sabbir; Pathan, Al-Sakib Khan
Many types of automated visual surveillance systems have been presented in the recent literature. Most of the schemes require custom equipment, or involve significant complexity and storage needs. After studying the area in detail, this work presents four novel algorithms to perform automated, real-time intruder detection in surveillance networks. Built using machine learning techniques, the proposed algorithms are adaptive and portable, do not require any expensive or sophisticated component, are lightweight, and efficient with runtimes of the order of hundredths of a second. Two of the proposed algorithms have been developed by us. With application to two complementary data sets and quantitative performance comparisons with two representative existing schemes, we show that it is possible to easily obtain high detection accuracy with low false positives.
This conference paper was presented in the 3rd International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014; Delhi; India; 24 September 2014 through 27 September 2014 [© 2014 Institute of Electrical and Electronics Engineers Inc.] The conference paper's definite version is available at: http:// 10.1109/ICACCI.2014.6968617
2014-01-01T00:00:00Z