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

  • 제17권8호
  • /
  • Pages.1919-1925
  • /
  • 2013
  • /
  • 2234-4772(pISSN)
  • /
  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
권호 표지
DOI QR코드

DOI QR Code

교통 환경 분석을 위한 움직임 기반 배경영상 추출

Motion-Based Background Image Extraction for Traffic Environment Analysis

  • Oh, Jeong-Su (Department of Image Science & Engineering, Pukyong National University)
  • 투고 : 2013.04.12
  • 심사 : 2013.05.30
  • 발행 : 2013.08.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문은 스쿨존 영역의 교통 환경 분석을 위한 배경영상 추출 알고리즘을 제안하고 있다. 제안된 알고리즘은 교통 환경에서 수시로 발생되는 밝기 변화와 정지 객체에 의한 문제를 해결하고 있다. 전자를 위해 고속 Sigma-Delta 알고리즘을 이용해 배경영상을 현 프레임으로 고속 갱신하고, 후자를 위해 직전 프레임과 오랜 시간의 평균 배경영상을 이용해 동적 영역을 검출하여 정지 객체를 배경영상에서 배제한다. 실험 결과 제안된 알고리즘은 기존 알고리즘들과 비교하여 밝기 변화에 빠르게 잘 적응하고 있고, 배경영역의 SAD (Sum of Absolute Differences)를 약40~80% 정도를 줄여주고 있다.

This paper proposes a background image extraction algorithm for traffic environment analysis in a school zone. The proposed algorithm solves the problems by level changes and stationary objects to be occurred frequently in traffic environment. For the former, it renews rapidly the background image toward the current frame using a fast Sima-Delta algorithm and for the latter, it excludes the stationary objects from the background image by detecting dynamic regions using a just previous frame and a background image averaged for a long time. The results of experiments show that the proposed algorithm adapts quickly itself to level change well, and reduces about 40~80% of SAD in background region in comparison with the conventional algorithms.

키워드

참고문헌

  1. B. Zhan, D. N. Monekosso, S. A Remagnino, and L. Q. Xu, "Crowd analysis: a survey," Machine Vision and Applications, vol. 19. pp. 345-357, 2008. https://doi.org/10.1007/s00138-008-0132-4
  2. L. M. Fuentes, and S. A. Velastin. "People tracking in surveillance applications," Image and Vision Computing, vol. 24, no. 11, pp. 1165-1171, 2006. https://doi.org/10.1016/j.imavis.2005.06.006
  3. A. B. Chan, "Privacy Preserving Crowd Monitoring: Counting People Without People Models or Tracking," in IEEE Conference on CVPR, pp. 1-7, 2008.
  4. S. Brutzer, B. Hoferlin, and G. Heidemann, "Evaluation of background subtraction techniques for video surveillance," in IEEE Conference on CVPR, pp. 1937-1944, 2011.
  5. R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, "Detecting Moving Objects, Ghosts, and Shadows in Video Streams," IEEE Trans. on PAMI, vol. 25, no. 10, pp. 1337-1342, 2003. https://doi.org/10.1109/TPAMI.2003.1233909
  6. T. Bouwmans, F. El Baf, and B. Vachon, "Background Modeling using Mixture of Gaussians for Foreground Detection-A Survey", Recent Patents on Computer Science, vol. 1, no. 3, pp. 219-237, 2008. https://doi.org/10.2174/2213275910801030219
  7. A. Mittal, and N. Paragios. "Motion-based Background Subtraction Using Adaptive Kernel Density Estimation." in IEEE Conference on CVPR, vol. 2, pp. 302-309, 2004.
  8. Z. Mayo and J. R. Tapamo, "Background Subtraction Survey for Highway Surveillance," in Proc. Annu. Symp. PRASA, pp. 77-82, 2009.
  9. J. Heikkila and O. Silven, "A Real-time System for Monitoring of Cyclists and Pedestrians." Image and Vision Computing, vol. 22, no. 7, pp. 563-570, 2004. https://doi.org/10.1016/j.imavis.2003.09.010
  10. A. Manzanera and J. C. Richefeu. "A New Motion Detection Algorithm Based on Sigma-delta Background Estimation," Pattern Recognition. Letter, vol. 28, no. 3, pp 320-328, 2007. https://doi.org/10.1016/j.patrec.2006.04.007