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Detecting Moving Targets from Traffic Video Based on the Dynamic Background Model

  • Bin Shao
  • Yunliang Jiang
  • Qing Shen
Chapter
  • 836 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6758)

Abstract

An efficient method to detect the moving target in traffic video based on the dynamic background model is proposed in this paper, after analyzing existing methods for target detection. The model of target detection is given firstly, then a rough set weighted classification method for video image is presented. Based on the video classifications, the background model is established on the historical data. The background judgment and moving object detection for video are done with this model and then the background model is updated with the current video. The experimental results show that this method can adapt the diversification of background and has high adaptability and precision. The processing speed can meet the requirement of real time detection.

Keywords

image processing rough set weighted classification transportation monitoring dynamic background mode 

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References

  1. 1.
    Foresti, G.L.: Object recognition and tracking for remote video surveillance. IEEE Transactions on Circuits and Systems for Video Technology 9(7), 1045–1062 (1999)CrossRefGoogle Scholar
  2. 2.
    Stringa, E., Regazzoni, C.S.: Real-time video-shot detection for scene surveillance applications. IEEE Transactions on Image Processing 9(1), 69–79 (2000)CrossRefGoogle Scholar
  3. 3.
    Stauffer, C., Crimson, W.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)CrossRefGoogle Scholar
  4. 4.
    Enkelmann, W.: Investigation of Multigrid Algorithms for the Estimation of Optical Flow Fields in Image Sequences. Computer Vision, Graphics and Image Processing 43, 150–177 (1998)CrossRefGoogle Scholar
  5. 5.
    Kreucher, C., Kastella, K., Hero, A.O.: Multi-Target Tracking Using the Joint Multi-Target Probability Density. IEEE Transactions on Aerospace and Electronic Systems 41(4), 1396–1414 (2005)CrossRefGoogle Scholar
  6. 6.
    Wenxiu, Z., et al.: Rough Set Theory and Method, pp. 17–25. Science publisher, Beijing (2001)Google Scholar
  7. 7.
    Yao, Y.Y.: Relational interpretations of neighborhood operators and rough set approximation. Information Sciences: An international Journal 111, 239–259 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Tsai, L.-W., Hsieh, J.-W., Fan, K.-C.: Vehicle detection using normalized color and edge map. In: IEEE International Conference on Image Processing (ICIP 2005), September 11–14 2005, vol. 2, pp. 598–601 (2005)Google Scholar
  9. 9.
    Jacques, J.C.S., Jung, C.R., Musse, S.R.: Background Subtraction and Shadow Detection in Grayscale Video Sequences Computer Graphics and Image Processing. In: 18th Brazilian Symposium on SIBGRAPI 2005, pp. 189–196 (2005)Google Scholar
  10. 10.
    Beynon, M.: Reducts within the variable precision rough sets model: A further investigation. European Journal of Operational Research 134, 592–605 (2001)CrossRefzbMATHGoogle Scholar
  11. 11.
    Youtian, D., Feng, C., Wenli, X.: Region-based moving shadow detection approach. Journal of Tsinghua University (Science and Technology) 1, 141–144 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bin Shao
    • 1
    • 2
  • Yunliang Jiang
    • 1
    • 2
  • Qing Shen
    • 1
  1. 1.School of Information and EngineeringHuzhou Teachers CollegeHuzhouChina
  2. 2.State Key Laboratory of CAD&CGZhejiang UniversityHangzhouChina

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