한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제24권1호
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  • Pages.22-27
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  • 2020
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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반려동물 모니터링을 위한 YOLO 기반의 이동식 시스템 설계

Design of YOLO-based Removable System for Pet Monitoring

  • Lee, Min-Hye (Department of Computer Information Engineering, Kunsan National University) ;
  • Kang, Jun-Young (Department of Electronic Engineering, Wonkwang University) ;
  • Lim, Soon-Ja (Department of Electronic Engineering, Wonkwang University)
  • 투고 : 2019.09.18
  • 심사 : 2019.10.07
  • 발행 : 2020.01.31
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⟨ 이전 논문 다음 논문 ⟩

초록

최근 1인 가구의 증가로 반려동물을 키우는 가구가 많아짐에 따라, 주인의 부재 시에도 반려동물의 상태나 행동을 모니터링하는 시스템에 대한 필요성이 요구되고 있다. 가정용 CCTV를 이용한 반려동물의 모니터링에는 지역적 한계가 있어, 다수의 CCTV를 필요로 하거나 반려동물의 행동반경을 제한하는 방법을 사용하게 된다. 본 논문에서는 반려동물 모니터링의 지역적 한계를 해결하고자 딥러닝을 이용하여 고양이를 검출하고 추적하는 이동식 시스템을 제안한다. 객체 검출 신경망 모델의 하나인 YOLO(You Look Only Once)를 이용하여 데이터셋을 학습하고, 이를 기반으로 라즈베리파이에 적용하여 영상에서 검출된 객체를 추적한다. 라즈베리파이와 노트북을 무선 랜으로 연결하고 고양이의 움직임과 상태를 실시간으로 확인이 가능한 이동식 모니터링 시스템을 설계하였다.

Recently, as the number of households raising pets increases due to the increase of single households, there is a need for a system for monitoring the status or behavior of pets. There are regional limitations in the monitoring of pets using domestic CCTVs, which requires a large number of CCTVs or restricts the behavior of pets. In this paper, we propose a mobile system for detecting and tracking cats using deep learning to solve the regional limitations of pet monitoring. We use YOLO (You Look Only Once), an object detection neural network model, to learn the characteristics of pets and apply them to Raspberry Pi to track objects detected in an image. We have designed a mobile monitoring system that connects Raspberry Pi and a laptop via wireless LAN and can check the movement and condition of cats in real time.

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