An Innovative Approach of Blending Security Features in Energy-Efficient Routing for a Crowded Network of Wireless Sensors

Pathan, Al-Sakib Khan

doi:10.1201/b16390-24

Adaptive algorithms for automated intruder detection in surveillance networks

Ahmed

¹

,

Pathan

²

,

Ahmed

³

2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

9

0

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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.2014.6968617Many 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.Publishe

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Learning Algorithms for Anomaly Detection from Images

Ahmed

¹

,

Pathan

²

,

Ahmed

³

2017

Biometrics

2

0

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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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Learning Algorithms for Anomaly Detection from Images

Ahmed

¹

,

Pathan

²

,

Ahmed

³

2015

International Journal of System Dynamics Applications

3

0

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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.

show abstract

An Innovative Approach of Blending Security Features in Energy-Efficient Routing for a Crowded Network of Wireless Sensors

Cited by 3 publications

References 30 publications

Adaptive algorithms for automated intruder detection in surveillance networks

Adaptive algorithms for automated intruder detection in surveillance networks

Learning Algorithms for Anomaly Detection from Images

Learning Algorithms for Anomaly Detection from Images

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