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
Highlights
An automatic indoor surveillance system should be able to detect dangerous events such as illegal intrusion for 24 h a day without human monitoring
The sensitivity of 5 mV/Pa and S/N ratio of 58 dB of in the frequency range of 500 to 8 kHz is sufficient for the microphone, and the sound pressure level (SPL) of 96 dB @ 10 cm @ 1W in the frequency range of 500 to 6 kHz is sufficient for the speaker
Most of the commercially available microphones and speakers embedded in a circuit television (CCTV) or smartphone camera can be used for the implementation of a sound field sensor
Summary
An automatic indoor surveillance system should be able to detect dangerous events such as illegal intrusion for 24 h a day without human monitoring. A single image sensor-based system often fails to detect the event because of various unstable illumination conditions. To solve this problem, multiple hybrid sensors can collaborate to increase the detection accuracy and stability [1]. To cope with low-illumination and a blind spot problem, a sound field sensor can efficiently detect an abnormal intrusion using a pair speaker and microphone even in a completely dark environment. To take advantages of both image and sound field sensors, we present a combined hybrid sensor-based surveillance system. The main contribution of this paper consists of two parts: (i) combination of an image sensor and sound field sensor to detect intrusion in a low illumination environment and (ii) power-efficient surveillance with illumination before an intrusion occurs. After demonstrating the performance of the proposed system using experimental results in Section 6, Section 7 concludes the paper
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