Journal of Advanced Marine Engineering and Technology

The Korean Society of Marine Engineering (한국마린엔지니어링학회)
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A study on implementation of background subtraction algorithm using LMS algorithm and performance comparative analysis

LMS algorithm을 이용한 배경분리 알고리즘 구현 및 성능 비교에 관한 연구

  • Kim, Hyun-Jun (Department of Electrical & Electronics Engineering, Korea Maritime and Ocean University) ;
  • Gwun, Taek-Gu (G2ICT) ;
  • Joo, Yank-Ick (Division of Electrical & Electronics Engineering, Korea Maritime and Ocean University) ;
  • Seo, Dong-Hoan (Division of Electrical & Electronics Engineering, Korea Maritime and Ocean University)
  • Received : 2014.12.03
  • Accepted : 2015.01.19
  • Published : 2015.01.31
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Abstract

Recently, with the rapid advancement in information and computer vision technology, a CCTV system using object recognition and tracking has been studied in a variety of fields. However, it is difficult to recognize a precise object outdoors due to varying pixel values by moving background elements such as shadows, lighting change, and moving elements of the scene. In order to adapt the background outdoors, this paper presents to analyze a variety of background models and proposed background update algorithms based on the weight factor. The experimental results show that the accuracy of object detection is maintained, and the number of misrecognized objects are reduced compared to previous study by using the proposed algorithm.

최근 정보화 및 컴퓨터 비전 기술의 발전과 함께 객체의 인식 및 추적 기능을 가진 CCTV시스템이 다양한 분야에서 연구되고 있다. 하지만 실외환경에서 발생할 수 있는 그림자의 변화, 조명의 변화, 움직이는 요소들과 같은 배경의 변화는 객체 인지성능에 영향을 주게 된다. 따라서 실외환경에서 배경의 변화를 실시간으로 갱신하기 위해 본 논문에서는 다양한 배경 모델링 기법들을 분석하고, 가중치를 기반으로 한 배경 갱신 알고리즘을 제안한다. 실험을 통해 제안한 알고리즘의 객체 검출 성능은 이전 연구의 객체 검출 성능을 유지하며, 오인식 된 객체 수가 이전 연구에 비해 감소됨을 확인하였다.

Keywords

References

  1. J. B. Jeon. "Intelligent CCTV Surveillance industry trends," Telecommunications Technology Association, vol. 142, pp. 50-55, 2012 (in Korean).
  2. S. Stephen, D. Lowe, and J. Little, "Vision-based mobile robot localization and mapping using scale-invariant features," Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2, pp. 2051-2058, 2001.
  3. X. Chen and Y. Jia, "Indoor localization for mobile robots using lampshade corners as landmarks : Visual system calibration, feature extraction and experiments," International Journal of Control, Automation and Systems, vol. 12, no. 6, pp. 1313-1322, 2014. https://doi.org/10.1007/s12555-013-0076-y
  4. C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, "Pfinder : real-time tracking of the human body," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 780-785, 1997. https://doi.org/10.1109/34.598236
  5. C. Stauffer and W. Grimson, "Adaptive background mixture models for real-time tracking," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246-252, 1999.
  6. Z. Zivkovic and F. Heijden, "Efficient adaptive density estimation per image pixel for the task of background subtraction," Pattern Recognition Letters, vol. 27, no. 7, pp. 773-780, 2006. https://doi.org/10.1016/j.patrec.2005.11.005
  7. E. Herrero-Jaraba, C. Orrite-Urunuela, and J. Senar, "Detected motion classification with a double-background and a neighborhood-based difference," Pattern recognition Letters, vol. 24, no. 12, pp. 2079-2092, 2003. https://doi.org/10.1016/S0167-8655(03)00045-X
  8. N. J. B. McFarlane and C. P. Schofield, "Segmentation and tracking of piglets in images," Machine Vision and Applications, vol. 8, no. 3, pp. 187-193, 1995. https://doi.org/10.1007/BF01215814
  9. R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, "Detecting moving objects, ghosts, and shadows in video streams," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1337-1342, 2003. https://doi.org/10.1109/TPAMI.2003.1233909
  10. N. M. Oliver, B Rosario, and A. Pentland, "A bayesian computer vision system for modeling human interactions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 831-843, 2000. https://doi.org/10.1109/34.868684
  11. M. Seki, T. Wada, H. Fujiwara, and K. Sumi, "Background detection based on the cooccurrence of image variations," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 65-72, 2003
  12. P. Noriega, B. Bascle, and O. Bernier, "Local kernel color histograms for background subtraction," International Conference on Computer Vision Theory and Applications, vol. 1, pp. 213-219, 2006
  13. M. Mason and Z. Duric, "Using histograms to detect and track objects in color video," 30th IEEE workshop on Applied Imagery Pattern Recognition, pp. 154-159, 2001
  14. J. D. Park and D. S. Kang "Implementation of an effective context-awareness system using object detection and classification algorithm," Journal of the Korean Institute of Information Technology, vol. 10, no. 2, pp. 191-197, 2012 (in Korean).
  15. D. H. Jang, A study on Fast Background Subtraction using integral Histogram, Ph,K. Department of Image Engineering, Chung-Ang University, Korea, 2008 (in Korean).

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