Robust event classification for a fiber optic perimeter intrusion detection system using level crossing features and artificial neural networks

Seedahmed S. Mahmoud; Jim Katsifolis

doi:10.1117/12.849607

23 April 2010 Robust event classification for a fiber optic perimeter intrusion detection system using level crossing features and artificial neural networks

Seedahmed S. Mahmoud, Jim Katsifolis

Author Affiliations +

Proceedings Volume 7677, Fiber Optic Sensors and Applications VII; 767708 (2010) https://doi.org/10.1117/12.849607
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Discriminating between intrusion and nuisance events without compromising sensitivity is a key performance parameter for any outdoor perimeter intrusion detection system. This is especially the case for intrusion and nuisance events which may have a similar impact on a perimeter fence. In this paper, a robust event classification system using features based on level crossings is presented for the detection and recognition of intrusion and non-intrusion events in an outdoor fence-mounted intrusion detection system for a range of operating environments and fence styles. The proposed classification system is applied to a distributed fiber-optic Mach Zehnder (MZ) mounted on a perimeter fence. It consists of a pre-processing stage employing high resolution time-frequency distribution, a novel event detection and feature extraction scheme based on level crossings, and a classification algorithm using a supervised neural network. Experimental results are presented showing accurate classification of different intrusion and non-intrusion events such as fence-climbing, fence-cutting, stone-throwing and stick-dragging. These results demonstrate the robustness of the proposed algorithm for various types of fence fabric and operating environments.

Citation Download Citation

Seedahmed S. Mahmoud and Jim Katsifolis "Robust event classification for a fiber optic perimeter intrusion detection system using level crossing features and artificial neural networks", Proc. SPIE 7677, Fiber Optic Sensors and Applications VII, 767708 (23 April 2010); https://doi.org/10.1117/12.849607

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