The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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A Study on the Background Image Updating Algorithm for Detecting Fast Moving Objects

고속 객체 탐지를 위한 배경화면 갱신 알고리즘에 관한 연구

  • 박종범 (한양여자대학교 정보경영과)
  • Received : 2016.05.17
  • Accepted : 2016.07.15
  • Published : 2016.08.31
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Abstract

A developed skill of an intelligent CCTV is also advancing by using its Image Acquisition Device. The most important part in the field of detecting comparatively fast moving objects is to effectively reduce the loads on updating the background image in order to achieve real-time update. However, the ability of the current general-purpose computer extracting the texture as characteristics has limits in application mostly due to the loads on processes. In this thesis, an algorithm for real-time updating the background image in an applied area such as detecting the fast moving objects like a driving car in a video of at least 30 frames per second is suggested and the performance is analyzed by a test of extracting object region from real input image.

영상취득 장치를 이용한 지능화된 감시 장치의 개발 기술 또한 발전하고 있다. 비교적 고속으로 움직이는 객체를 탐지해야 하는 분야에서 무엇보다 중요한 것은 배경영상 갱신에 대한 부하를 효과적으로 줄여서 실시간적으로 갱신할 수 있어야 하는데 현재 범용 컴퓨터 능력으로는 질감 등을 특징으로 추출하는 방법 등은 대부분 연산처리의 부하 때문에 적용상의 한계가 있다. 본 논문에서는 적어도 초당 30프레임의 카메라 영상에서 주행 중인 자동차와 같이 고속으로 움직이는 객체를 탐지하는 응용영역에서 실시간으로 배경 영상을 갱신하는 알고리즘을 제시하고, 실제 입력영상에서 객체 영역을 추출하는 시험을 통해 성능을 분석하였다.

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References

  1. Park J. B.(2013), "A Study on the Revised Method using Normalized RGB Features in the Moving Object Detection by Background Subtraction," Journal of The Korea Institute of Intelligent Transport Systems, vol. 12, no. 6, pp.108-115. https://doi.org/10.12815/kits.2013.12.6.108
  2. Jang T. W.(2013), "A Study on Intelligent CCTV Surveillance System Based on Realtime Tracking Technology," University of Soongsil, Ph. D. Dissertation.
  3. Kim Y. J. and Kim D. H.(2013), "Smart Phone Based Image Processing Methods for Motion Detection of a Moving Object via a Network Camera," Journal of Control. Robotics and Systems, vol. 19, no. 1, pp.65-71. https://doi.org/10.5302/J.ICROS.2013.19.1.065
  4. Stauffer C. and Grimson W. E. L.(1999), "Adaptive Background Mixture Models for Real-Time Tracking," Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp.246-252.
  5. Image Processing Toolbox, Chapter 9.(2001), Morphological Operations, The Mathworks.
  6. GoldluFcke B. and Magnor M. A.(2003), "Joint 3D Reconstruction and Background Separation in Multiple Views using Graph Cuts," Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, vol. 1, pp.683-688.
  7. Sormann M., Zach C. and Karner K.(2006), "Graph Cut based Multiple View Segmentation for 3D Reconstruction," Proceedings of IEEE International Symposium on 3D Data Processing, Visualization, and Transmission, pp.1085-1092.
  8. Min B. M. and Oh S.(2006), "A Study on Object Tracking using Variable Search Block Algorithm," The KIPS Transactions. Part B Part B / v.13B, no. 4, pp.463-470. https://doi.org/10.3745/KIPSTB.2006.13B.4.463