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SFMOG : Super Fast MOG Based Background Subtraction Algorithm

SFMOG : 초고속 MOG 기반 배경 제거 알고리즘

  • Song, Seok-bin (Dept. of Computer Engineering, Seokyeong University) ;
  • Kim, Jin-Heon (Dept. of Computer Engineering, Seokyeong University)
  • Received : 2019.12.02
  • Accepted : 2019.12.15
  • Published : 2019.12.31

Abstract

Background subtraction is the major task of computer vision and image processing to detect changes in video. The best performing background subtraction is computationally expensive that cannot be used in real time in a typical computing environment. The proposed algorithm improves the background subtraction algorithm of the widely used MOG with the image resizing algorithm. The proposed image resizing algorithm is designed to drastically reduce the amount of computation and to utilize local information, which is robust against noise such as camera movement. Experimental results of the proposed algorithm have a classification capability that is close to the state of the art background subtraction method and the processing speed is more than 10 times faster.

배경 제거는 동영상에서 변화를 감지하는 컴퓨터 비전 및 이미지 처리의 주요 작업이다. 최상의 성능을 가지는 배경 제거 방법은 일반적인 컴퓨팅 환경에서 실시간으로 사용할 수 없을 만큼 계산량이 많다. 제안하는 알고리즘은 널리 사용되는 MOG 기반의 배경 제거 알고리즘을 이미지 크기 조정 알고리즘으로 개선했다. 제안된 이미지 크기 조정 알고리즘은 계산량을 대폭 감소시키고 지역 정보를 활용하도록 설계해 카메라 잡음에 강력하다. 제안된 알고리즘의 실험결과는 최신 배경 제거 방법에 근접하는 분류능력과 13배 이상 빠른 처리 속도를 가진다.

Keywords

References

  1. T. Bouwmans, "Recent Advanced Statistical Background Modeling for Foreground Detection: A Systematic Survey," Recent Patents on Computer Science, vol.4, No.3, 2011. DOI: 10.2174/2213275911104030147
  2. A. Sobral and A. Vacavan, "A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos," Computer Vision and Image Understanding, vol.122, pp.4-21, 2014. DOI: 10.1016/j.cviu.2013.12.005
  3. K Sehairi and F Chouireb, "Comparative study of motion detection methods for video surveillance systems," Journal of Electronic Imaging, vol.26, no.2, 2017. DOI: 10.1117/1.JEI.26.2.023025
  4. T. Bouwmans, "Traditional and recent approaches in background modeling for foreground detection: An overview," Computer Science Review, vo.11-12, pp.31-66, 2014. DOI: 10.1016/j.cosrev.2014.04.001
  5. Garcia-Garcia, Belmar, Thierry Bouwmans, and Alberto Jorge Rosales Silva. "Background Subtraction in Real Applications: Challenges, Current Models and Future Directions," Computer Science Review, vol35, 2020. DOI: 10.1016/j.cosrev.2019.100204
  6. Zoran Zivkovic and Ferdinand van der 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. DOI: 10.1016/j.patrec.2005.11.005
  7. Y. Wang, P.-M. Jodoin, F. Porikli, J. Konrad, Y. Benezeth, and P. Ishwar, "CDnet 2014: An Expanded Change Detection Benchmark Dataset," IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.387-394, 2014. DOI: 10.1109/CVPRW.2014.126
  8. C. Stauffer, W. E. L. Grimson, "Adaptive background mixture models for real-time tracking," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.2, 1999. DOI: 10.1109/CVPR.1999.784637
  9. 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. DOI: 10.2174/2213275910801030219
  10. S. Varadarajan, P. Miller and H. Zhou, "Spatial mixture of Gaussians for dynamic background modelling," Advanced Video and Signal Based Surveillance (AVSS), 2013 10th IEEE International Conference, pp.63-68, 2013. DOI: 10.1109/AVSS.2013.6636617
  11. S. Varadarajan, P. Miller and H. Zhou, "Regionbased Mixture of Gaussians modelling for foreground detectionin dynamic scenes," Pattern Recognition, vol.48, pp.2488-3503, 2015. DOI: 10.1016/j.patcog.2015.04.016
  12. I. Martins, P. Carvalho, L. Corte-Real, and J. Alba-Castro, "BMOG: Boosted Gaussian Mixture Model with Controlled Complexity," Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2017), pp.50-57, 2017. DOI: 10.1007/s10044-018-0699-y
  13. R. Wang, F. Bunyak, G. Seetharaman and K. Palaniappan "Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models," IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2014. DOI: 10.1109/CVPRW.2014.68
  14. I. Lissner, P. Urban, "Toward a unified color space for perception-based imageprocessing," IEEE Transactions on Image Processing, vol.21, no.3, pp.1153-1168, 2012. DOI: 10.1109/TIP.2011.2163522
  15. M. Balcilar, M. F. Amasyali, A. C. Sonmez, "Moving object detection usinglab2000hl color space with spatial and temporal smoothing," Applied Mathematics & Information Sciences, vol.8, no.4, pp.1755-1766, 2014. DOI: 10.12785/amis/080433
  16. 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
  17. S. Bianco, G. Ciocca and R. Schettini, "Combination of Video Change Detection Algorithms by Genetic Programming," in IEEE Transactions on Evolutionary Computation, vol.21, no.6, pp.914-928, 2017. DOI: 10.1109/TEVC.2017.2694160
  18. Jiang S, Lu X. "WeSamBE: A Weight-Sample-Based Method for Background Subtraction[J]," IEEE Transactions on Circuits and Systems for Video Technology, vol.28, no.9, pp.2105-2115, 2017. DOI: 10.1109/TCSVT.2017.2711659
  19. P.-L. St-Charles, G.-A. Bilodeau, R. Bergevin, "A Self-Adjusting Approach to Change Detection Based on Background Word Consensus," IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. DOI: 10.1109/WACV.2015.137
  20. P.-L. St-Charles, G.-A. Bilodeau, R. Bergevin, "SuBSENSE: A Universal Change Detection Method with Local Adaptive Sensitivity," IEEE Transactions on Image Processing, 2014. DOI: 10.1109/WACV.2015.137