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Symmetry 2018, 10(6), 181; https://doi.org/10.3390/sym10060181

Hybrid Sensor Network-Based Indoor Surveillance System for Intrusion Detection

1
Department of Image, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea
2
Multidisciplinary Sensor Research Group, Electronics and Telecommunications Research Institute (ETRI), Gajeong-ro 218, Yuseong-gu, Daejeon 34129, Korea
*
Author to whom correspondence should be addressed.
Received: 19 April 2018 / Revised: 20 May 2018 / Accepted: 21 May 2018 / Published: 23 May 2018
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Abstract

This paper presents a novel hybrid sensor-based intrusion detection system for low-power surveillance in an empty, sealed indoor space with or without illumination. The proposed system includes three functional steps: (i) initial detection of an intrusion event using a sound field sensor; (ii) automatic lighting control based on the detected event, and (iii) detection and tracking the intruder using an image sensor. The proposed hybrid sensor-based surveillance system uses a sound field sensor to detect an abnormal event in a very low-light or completely dark environment for 24 h a day to reduce the power consumption. After detecting the intrusion by the sound sensor, a collaborative image sensor takes over an accurate detection and tracking tasks. The proposed hybrid system can be applied to various surveillance environments such as an office room after work, empty automobile, safety room in a bank, and armory room. This paper deals with fusion of computer-aided pattern recognition and physics-based sound field analysis that reflects the symmetric aspect of computer vision and physical analysis View Full-Text
Keywords: hybrid sensor; intrusion detection; image sensor; sound field sensor hybrid sensor; intrusion detection; image sensor; sound field sensor
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Park, H.; Park, J.; Kim, H.; Lee, S.Q.; Park, K.-H.; Paik, J. Hybrid Sensor Network-Based Indoor Surveillance System for Intrusion Detection. Symmetry 2018, 10, 181.

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