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

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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An Method for Inferring Fine Dust Concentration Using CCTV

CCTV를 이용한 미세먼지 농도 유추 방법

  • Hong, Sunwon (Department of Electronic Engineering, Korea National University of Transportation) ;
  • Lee, Jaesung (Department of Electronic Engineering, Korea National University of Transportation)
  • Received : 2019.08.20
  • Accepted : 2019.08.30
  • Published : 2019.10.31
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Abstract

This paper proposes a method for measuring fine dust concentration through digital processing of images captured by only existing CCTVs without additional equipment. This image processing algorithm consists of noise reduction, edge sharpening, ROI setting, edge strength calculation, and correction through HSV conversion. This algorithm is implemented using the C ++ OpenCV library. The algorithm was applied to CCTV images captured over a month. The edge strength values calculated for the ROI region are found to be closely related to the fine dust concentration data. To infer the correlation between the two types fo data, a trend line in the form of a power equation is established using MATLAB. The number of data points deviating from the trend line accounts for around 12.5%. Therefore, the overall accuracy is about 87.5%.

본 논문에서는 추가 설비 없이 기존 CCTV 영상을 디지털 영상 처리를 통하여 미세먼지 농도를 측정하는 방법을 제안한다. 영상처리 알고리즘은 노이즈 제거, 샤프닝, ROI 지정, 엣지 강도 계산, HSV 변환을 통한 보정 순으로 구성되며 C++ OpenCV 라이브러리를 이용해 구현하였다. 한달동안 캡쳐한 CCTV 이미지들에 본 알고리즘을 적용한 결과 ROI 영역에 대해 계산된 엣지 강도는 미세먼지 농도와 밀접한 관계가 있는 것으로 나타났다. 두 데이터간 상관관계를 추론하고자 MATLAB을 이용하여 거듭제곱 방정식 형태의 추세선을 수립하였으며 그 추세선으로부터 이탈한 데이터 포인트들의 개수는 12.5% 내외로 나타나 전체적으로 약 87.5%의 정확도를 보였다.

Keywords

References

  1. B. S. Yang, CCTV for Sensing Neighborhood Information and Method of Determining Thereof, KR Patent KR101873924B1, to Globaltelecom, Co.. Ltd., 2017.
  2. Namsan Tower CCTV [Internet]. Available: https://www.youtube.com/channel/UC90AkGrousC-CDBcCL8U aSg.
  3. Air Korea Real-Time Air Quality [Internet]. Available: https://www.airkorea.or.kr.
  4. OpenCV team. Open Source Computer Vision Library [Internet]. Available: https://docs.opencv.org/master/d9/df8/tutorial_root.html.
  5. D. Kim, C++ API OpenCV Programming, Dec. 2016.
  6. J. Lee, Basics of Traffic Image Processing, Mar. 2013.
  7. P. Topno, and G. Murmu, "An Improved Edge Detection Method based on Median Filter," Devices for Integrated Circuit (DevIC), Kalyani, India, pp. 378-381, Mar. 2019.
  8. J. Lian, "Two Adaptive Schemes for Image Sharpening," IEEE 2nd International Conference on Information and Computer Technologies (ICICT), Kahului, HI, USA, 2019, pp. 122-125, Mar. 2019.
  9. Y. Song, B. Ma, W. Gao, and S. Fan, "Medical Image Edge Detection Based on Improved Differential Evolution Algorithm and Prewitt Operator," Acta Microscopica, vol. 28, no. 1, pp. 30-39, Feb. 2019.
  10. S. Kang, N. Kim, and B. Lee, "Fine Dust Forecast Based on Recurrent Neural Networks," International Conference on Advanced Communication Technology (ICACT), PyeongChang Kwangwoon_Do, Korea (South), pp. 456-459, Feb. 2019.