Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Adaptive Counting Line Detection for Traffic Analysis in CCTV Videos

CCTV영상 내 교통량 분석을 위한 적응적 계수선 검출 방법

  • Jung, Hyeonseok (Department of Electrical and Electronic Engineering, Konkuk University) ;
  • Lim, Seokjae (Department of Electrical and Electronic Engineering, Konkuk University) ;
  • Lee, Ryong (Research Data Sharing Center, Korea Institute of Science and Technology Information) ;
  • Park, Minwoo (Research Data Sharing Center, Korea Institute of Science and Technology Information) ;
  • Lee, Sang-Hwan (Research Data Sharing Center, Korea Institute of Science and Technology Information) ;
  • Kim, Wonjun (Department of Electrical and Electronic Engineering, Konkuk University)
  • 정현석 (건국대학교 전기전자공학부) ;
  • 임석재 (건국대학교 전기전자공학부) ;
  • 이용 (한국과학기술정보연구원 연구데이터공유센터) ;
  • 박민우 (한국과학기술정보연구원 연구데이터공유센터) ;
  • 이상환 (한국과학기술정보연구원 연구데이터공유센터) ;
  • 김원준 (건국대학교 전기전자공학부)
  • Received : 2019.09.11
  • Accepted : 2019.11.21
  • Published : 2020.01.30
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Abstract

Recently, with the rapid development of image recognition technology, the demand for object analysis in road CCTV videos is increasing. In this paper, we propose a method that can adaptively find the counting line for traffic analysis in road CCTV videos. First, vehicles on the road are detected, and the corresponding positions of the detected vehicles are modeled as the two-dimensional pointwise Gaussian map. The paths of vehicles are estimated by accumulating pointwise Gaussian maps on successive video frames. Then, we apply clustering and linear regression to the accumulated Gaussian map to find the principal direction of the road, which is highly relevant to the counting line. Experimental results show that the proposed method for detecting the counting line is effective in various situations.

최근 영상 인식 기술의 급격한 발전으로 도로 교통 CCTV영상 내에서의 객체 분석 요구가 증대되고 있다. 본 논문에서는 도로 교통 CCTV영상 내의 교통량 분석을 위한 계수선(Counting Line)을 도로의 형태에 따라 적응적으로 검출할 수 있는 방법을 제안한다. 우선 도로 위의 차량을 검출하고 검출한 차량의 위치를 이차원 가우시안 형태의 함수로 모델링 한 후, 이를 연속된 프레임 상에서 누적하여 차량의 이동 경로를 표현하는 누적 가우시안 지도를 얻어낸다. 이렇게 얻어낸 누적 가우시안 지도에 군집화 및 선형 회귀를 적용하여 도로의 주방향을 구하고, 이 주방향을 이용하여 최종적으로 교통량 분석을 위한 계수선을 검출한다. 다양한 CCTV상황에서 제안하는 방법을 적용하였을 때 계수선을 효과적으로 검출할 수 있는 것을 실험적으로 확인하였다.

Keywords

References

  1. S. Kamkar and R. Safabakhsh, "Vehicle detection, counting and classification in various conditions," IET Intelligent Transport Systems, Vol.10, No.6, pp.406-413, July 2016. https://doi.org/10.1049/iet-its.2015.0157
  2. S. Bouaich, M. A. Mahraz, J. Riffi, and H. Tairi, "Vehicle counting system in real-time," Proceeding of IEEE International Conference on Intelligent Systems and Computer Vision (ISCV), pp.1-4, 2018.
  3. J. -P. Lin and M. -T. Sun, "A YOLO-based traffic counting system," Proceeding of IEEE Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp.82-85, 2018.
  4. S. Ren, K. He, R. Girshick, and J. Sun, "Faster r-cnn: towards real-time object detection with region proposal networks," Proceeding of Advances in Neural Information Processing Systems (NeurIPS), pp.91-99, 2015.
  5. S. Ren, K. He, R. Girshick, and J. Sun, "Faster r-cnn: towards real-time object detection with region proposal networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.39, No.6, pp. 1137-1149, June 2017. https://doi.org/10.1109/TPAMI.2016.2577031
  6. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. -Y. Fu, and A. C. Berg, "SSD: single shot multibox detector," Proceeding of European Conference on Computer Vision (ECCV), pp.21-37, 2016.
  7. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: unified, real-time object detection," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.779-788, 2016.
  8. J. Redmon and A. Farhadi, "YOLO9000: better, faster, stronger," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.7263-7271, 2017.
  9. J. Redmon and A. Farhadi, "YOLOv3: an incremental improvement," arXiv preprint arXiv:1804.02767, April 2018.
  10. Daejeon metropolitan city road traffic CCTV, http://traffic.daejeon.go.kr/map/trafficInfo/cctv.do (accessed Feb. 1, 2019).
  11. T. -Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollar, "Microsoft coco: common objects in context," Proceeding of European Conference on Computer Vision (ECCV), pp.740-755, 2014.
  12. M. Ester, H. -P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," Proceeding of International Conference on Knowledge Discovery and Data Mining (KDD), pp.226-231, 1996.
  13. K. Pearson, "Mathematical contributions to the theory of evolution.-III. Regression, heredity and panmixia," Philosophical Transactions of the Royal Society of London, Series A, Vol.187, pp.253-318, 1896. https://doi.org/10.1098/rsta.1896.0007
  14. O. J. Dunn and V. A. Clark, Applied Statistics: Analysis of Variance and Regression, Wiley, New York, 1974.
  15. J. L. Rodgers and W. A. Nicewander, "Thirteen ways to look at the correlation coefficient," The American Statistician, Vol.42, No.1, pp.59-66, February 1988. https://doi.org/10.1080/00031305.1988.10475524
  16. M. M. Mukaka, "A guide to appropriate use of correlation coefficient in medical research," Malawi Medical Journal, Vol.24, No.3, pp.69-71, September 2012.
  17. B. Ratner, "The correlation coefficient: its values range between +1/-1, or do they?," Journal of Targeting, Measurement and Analysis for Marketing, Vol.17, No.2, pp.139-142, May 2009. https://doi.org/10.1057/jt.2009.5