Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Intruder Detection System Based on Pyroelectric Infrared Sensor

PIR 센서 기반 침입감지 시스템

  • Jeong, Yeon-Woo (School of Electronic & Electrical Engineering, Hongik University) ;
  • Vo, Huynh Ngoc Bao (School of Electronic & Electrical Engineering, Hongik University) ;
  • Cho, Seongwon (School of Electronic & Electrical Engineering, Hongik University) ;
  • Cuhng, Sun-Tae (School of Information Communication Electrical Engineering, Soongsil University)
  • 정연우 (홍익대학교 전자전기공학부) ;
  • ;
  • 조성원 (홍익대학교 전자전기공학부) ;
  • 정선태 (숭실대학교 스마트시스템소프트웨어학과)
  • Received : 2016.08.29
  • Accepted : 2016.10.18
  • Published : 2016.10.25
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Abstract

The intruder detection system using digital PIR sensor has the problem that it can't recognize human correctly. In this paper, we suggest a new intruder detection system based on analog PIR sensor to get around the drawbacks of the digital PIR sensor. The analog type PIR sensor emits the voltage output at various levels whereas the output of the digitial PIR sensor is binary. The signal captured using analog PIR sensor is sampled, and its frequency feature is extracted using FFT or MFCC. The extracted features are used for the input of neural networks. After neural network is trained using various human and pet's intrusion data, it is used for classifying human and pet in the intrusion situation.

기존 디지털 출력 방식의 PIR 센서를 이용한 침입감지 시스템은 사람이 아닌 다른 물체에 대한 침입 탐지 오류가 많았다. 본 논문은 이를 극복하기 위하여 아날로그 출력 방식의 PIR 센서 기반 침입 감지 시스템을 제안한다. 아날로그 방식 PIR 센서는 임계값을 기준으로 이진 출력값 대신, 일정 범위 내의 다양한 전압 준위로 출력값을 내보낸다. 아날로그 PIR 센서를 이용하여 획득된 신호의 샘플링된 신호값으로부터 FFT(Fast Fourier Transform) 또는 MFCC(Mel-frequency cepstrum codfficents)을 이용하여 신호의 주파수 성분을 추출하여, 인공 신경회로망(Artificial Neural Network)의 특징벡터로 사용된다. 다양한 인간의 움직임과 애완동물의 움직임에 대한 신호 패턴들을 학습한 인공 신경회로망을 통해서 침입상황에서 침입한 객체가 사람인지 애완동물인지 판별하게 된다.

Keywords

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