Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
권호 표지

An Improved Multiple Interval Pixel Sampling based Background Subtraction Algorithm

개선된 다중 구간 샘플링 배경제거 알고리즘

  • Mahmood, Muhammad Tariq (Korea University of Technology and Education, School of Computer Science and Engineering) ;
  • Choi, Young Kyu (Korea University of Technology and Education, School of Computer Science and Engineering)
  • Received : 2019.07.10
  • Accepted : 2019.09.23
  • Published : 2019.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Foreground/background segmentation in video sequences is often one of the first tasks in machine vision applications, making it a critical part of the system. In this paper, we present an improved sample-based technique that provides robust background image as well as segmentation mask. The conventional multiple interval sampling (MIS) algorithm have suffer from the unbalance of computation time per frame and the rapid change of confidence factor of background pixel. To balance the computation amount, a random-based pixel update scheme is proposed and a spatial and temporal smoothing technique is adopted to increase reliability of the confidence factor. The proposed method allows the sampling queue to have more dispersed data in time and space, and provides more continuous and reliable confidence factor. Experimental results revealed that our method works well to estimate stable background image and the foreground mask.

Keywords

References

  1. B. Lee, Y. Chu and Y. Choi, "A Background Subtraction Algorithm for Fence Monitoring Surveillance Systems," Journal of the Semiconductor & Display Technology, Vol. 14, No. 3. September 2015.
  2. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, "Pfinder: Real-Time Tracking of the Human Body," IEEE Trans. on PAMI, Vol. 19, No. 7, pp. 780-785, July 1997. https://doi.org/10.1109/34.598236
  3. C. Stauffer and W. Grimson, "Adaptive Background Mixture Models for Real-Time Tracking," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, Vol. 2, pp. 246-252, 1999.
  4. M. Heikkila and M. Pietikainen, "A Texture-Based Method for Modeling the Background and Detecting Moving Objects," IEEE Trans. on PAMI, Vol. 28, No. 4, pp. 657-662, April 2006. https://doi.org/10.1109/TPAMI.2006.68
  5. H. Wang and D. Suter, "A consensus-based method for tracking: Modelling background scenario and foreground appearance," Pattern Recognition, Vol. 40, No. 3, pp. 1091-1105, 2007. https://doi.org/10.1016/j.patcog.2006.05.024
  6. D. Lee and Y. Choi, "Background subtraction algorithm based on Multiple Interval Pixel Sampling," KIPS trans. On Software and Data Engineering, Vol. 2, No. 1 , pp. 27-34, 2013. https://doi.org/10.3745/KTSDE.2013.2.1.027
  7. O. Barnich and M. Van Droogenbroeck, "ViBe: A universal background subtraction algorithm for video sequences," IEEE Trans. on Image Processing, Vol. 20, No. 6, pp. 1709-1724, June 2011. https://doi.org/10.1109/TIP.2010.2101613
  8. P.-L. St-Charles, G.-A. Bilodeau, and R. Bergevin. Subsense: A universal change detection method with local adaptive sensitivity. IEEE Trans. on Image Processing, Vol. 24, No. 1, pp. 359-373, 2015. https://doi.org/10.1109/TIP.2014.2378053
  9. M. Babaee, D. Dinh and G. Rigoll, "Deep Learning A Deep Convolutional Neural Network for Background Subtraction," arXiv:1702.01731v1 6 Feb 2017.
  10. Y. Chu and Y. Choi, "A Deep Learning based IOT Device Recognition System," Journal of the Semiconductor & Display Technology, Vol. 18, No. 2. June 2019.
  11. J. Hahn and Y. Choi, "A Real-time Vehicle Localization Algorithm for Autonomous Parking System," Journal of the Semiconductor & Display Technology, Vol. 10, No. 2. June 2011.
  12. Y. Pritch, A. Rav-Acha and S. Peleg, "Non-Chronological Video Synopsis and Indexing," IEEE Trans. on PAMI, Vol. 30, pp. 1971-1984, 2008. https://doi.org/10.1109/TPAMI.2008.29