Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
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Moving Target Detection based on Frame Subtraction and Morphological filter with Drone Imaging

프레임 감산과 형태학적 필터를 이용한 드론 영상의 이동표적의 검출

  • Lee, Min-Hyuck (School of Computer and Communication Eng., Daegu University) ;
  • Yeom, SeokWon (School of Computer and Communication Eng., Daegu University)
  • 이민혁 (대구대학교 정보통신공학부) ;
  • 염석원 (대구대학교 정보통신공학부)
  • Received : 2018.12.15
  • Accepted : 2018.12.28
  • Published : 2018.12.31
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Abstract

Recently, the use of drone has been increasing rapidly in many ways. A drone can capture remote objects efficiently so it is suitable for surveillance and security systems. This paper discusses three methods for detecting moving vehicles using a drone. We compare three target detection methods using a background frame, preceding frames, or moving average frames. They are subtracted from a current frame. After the frame subtraction, morphological filters are applied to increase the detection rate and reduce the false alarm rate. In addition, the false alarm region is removed based on the true size of targets. In the experiments, three moving vehicles were captured by a drone, and the detection rate and the false alarm rate were obtained by three different methods and the results are compared.

최근 드론의 활용이 여러 방면에서 급격하게 증가하고 있다. 드론은 원격으로 표적을 효율적으로 촬영할 수 있어 감시와 보안 시스템에 유용하다. 본 논문은 드론을 이용한 움직이는 차량을 검출하는 세 가지 방법을 연구한다. 배경 영상, 선행 프레임, 또는 이동 평균 프레임과 현재 프레임과의 감산 기법을 이용한 표적 검출을 비교한다. 프레임 감산 후 형태학적 필터링을 적용하여 검출률을 높이고 오보율을 감소시킨다. 또한 표적의 크기를 알고 있다는 가정 하에 영역크기 비교를 통하여 오경보 영역을 제거한다. 실험에서는 움직이는 3대의 자동차를 드론으로 촬영하여 앞서 제시한 방법에 따라 표적을 검출하고 각각 검출율과 오보율을 구하였다.

Keywords

References

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