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키넥트 깊이 정보를 이용한 개별 돼지의 탐지

Individual Pig Detection Using Kinect Depth Information

  • 최장민 (고려대학교 컴퓨터정보학과) ;
  • 이종욱 (고려대학교 컴퓨터정보학과) ;
  • 정용화 (고려대학교 컴퓨터정보학과) ;
  • 박대희 (고려대학교 컴퓨터정보학과)
  • 투고 : 2016.08.09
  • 심사 : 2016.08.29
  • 발행 : 2016.10.31

초록

밀집된 돈방에서 사육되는 돼지의 공격적 행동들은 돼지의 성장에 심각한 악영향을 주고, 이는 농가의 경제적 손실로 이어진다. 따라서 농가의 생산성 하락에 따른 경제적 손실과 직결되는 돈방 내의 비정상 상황들을 지속적으로 모니터링 할 수 있는 IT기반의 영상 감시 시스템이 요구된다. 본 논문에서는 돼지의 행동 분석 이전에 필수적으로 선행되어야만 하는 개별 돼지의 탐지를 위한 키넥트 카메라 기반의 새로운 모니터링 시스템을 제안한다. 제안된 시스템은 다음과 같다. 1) 키넥트 카메라로부터 취득한 깊이 영상에서 배경 차영상 기법과 깊이 임계값을 이용하여 서있는 돼지만을 탐지함으로써 영상 내의 탐색영역을 축소한다, 2) 서있는 돼지들 중에서 움직임이 있는 돼지들만을 관심영역으로 설정하여 탐지한다. 3) 서서 움직이는 돼지들 사이에서 발생하는 근접 문제를 깊이정보를 이용한 등고선기법을 제안 적용하여 돼지객체 탐지를 완성한다. 실제 세종에 위치한 한 돈사에서 취득한 깊이 영상 정보를 이용하여 본 논문에서 제안하는 시스템의 성능을 실험적으로 검증한다.

Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. In this paper, we propose a new Kinect camera-based monitoring system for the detection of the individual pigs. The proposed system is characterized as follows. 1) The background subtraction method and depth-threshold are used to detect only standing-pigs in the Kinect-depth image. 2) The moving-pigs are labeled as regions of interest. 3) A contour method is proposed and applied to solve the touching-pigs problem in the Kinect-depth image. The experimental results with the depth videos obtained from a pig farm located in Sejong illustrate the efficiency of the proposed method.

키워드

참고문헌

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피인용 문헌

  1. Multi-Pig Part Detection and Association with a Fully-Convolutional Network vol.19, pp.4, 2019, https://doi.org/10.3390/s19040852