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Panoramic Gaussian Mixture Model and large-scale range background substraction method for PTZ camera-based surveillance systems

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Abstract

In this paper, we present a novel approach for constructing a large-scale range panoramic background model that provides fast registration of the observed frame and localizes the foreground targets with arbitrary camera direction and scale in a Pan–tilt–zoom (PTZ) camera-based surveillance system. Our method consists of three stages. (1) In the first stage, a panoramic Gaussian mixture model (PGMM) of the PTZ camera’s field of view is generated off-line for later use in on-line foreground detection. (2) In the second stage, a multi-layered correspondence ensemble is generated off-line from frames captured at different scales which is used by the correspondence propagation method to register observed frames online to the PGMM. (3) In the third stage, foreground is detected and the PGMM is updated. The proposed method has the capacity to deal with the PTZ camera’s ability to cover a wide field of view (FOV) and large-scale range. We demonstrate the advantages of the proposed PGMM background subtraction method by incorporating it with a tracking system for surveillance applications.

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References

  1. Darvish, P., Varcheie, Z., Bilodeau, G.-A.: People tracking using a network-based PTZ camera. In: Machine Vision and Applications (2010) (Sept. 2010)

  2. Xu, Y., Song, D.: Systems and algorithms for autonomous and scalable crowd surveillance using robotic PTZ cameras assisted by a wide-angle camera. In: Auton Robot (2010) vol. 29, pp. 53–66. (Apr. 2010)

  3. Heikkila M., Pietikanen M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)

    Article  Google Scholar 

  4. Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE 90(7), pp. 1151–1163 (2002)

    Google Scholar 

  5. Hu, W., Gong, H., Zhu, S.-C., Wang, Y.: An integrated background model for video surveillance based on primal sketch and 3D scene geometry. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008 (2008)

  6. Kim, K., Chalidabhongse, T., Harwood, D., Davis, L.: Background modeling and subtraction by codebook construction, ICIP 2004 (2004)

  7. Friedman, N., Russell, S.: Image segmentation in video sequences: A probabilistic approach. UAI, pp. 175–181 (1997)

  8. Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. CVPR, pp. 246–252 (1999)

  9. Helmut, G., Horst, B.: On-line boosting and vision. In: Computer Vision and Pattern Recognition (CVPR) (2006)

  10. Kang, S.P., Joonki, K.A., Abidi, B., Mongi, A.A.: Real-time video tracking using PTZ cameras. In: Proc. of SPIE 6th International Conference on Quality Control by Artificial Vision, Gatlinburg, TN, vol.5132, pp. 103–111, May (2003)

  11. Wu, S., Zhao, T., Broaddus, C., Yang, C., Aggarwal, M.: Robust Pan, Tilt and Zoom Estimation for PTZ Camera by Using Meta Data and/or Frame-to-Frame Correspondences. In: Control, Automation, Robotics and Vision (2006)

  12. Micheloni C., Foresti Gian L.: Real-time image processing for active monitoring of wide areas. J. Vis. Commun. Image Represent. 17(3), 589–604 (2006)

    Article  Google Scholar 

  13. Azzari, P., Di Stefano, L., Bevilacqua, A.: An effective real-time mosaicing algorithm apt to detect motion through background subtraction using a ptz camera. In: IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 511–516 (Sept. 2005)

  14. Bevilacqua, A., Azzari, P.: High-quality real time motion detection using ptz cameras. In: IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1923 (Nov. 2006)

  15. Sinha S.N., Pollefeys M.: Pan–tilt–zoom camera calibration and high-resolution mosaic generation. Comput. Vis. Image Understand. 103(3), 170–183 (2006)

    Article  Google Scholar 

  16. Chen C.-H., Yao Y., Page D., Abidi B.R., Koschan A., Abidi M.: Heterogeneous fusion of omnidirectional and PTZ cameras for multiple object tracking. IEEE Trans Circuits Syst Video Technol 18(8), 1052–1063 (2008)

    Article  Google Scholar 

  17. Chen, C.-C., Yao, Y., Drira, A., Koschan, A., Abidi, M.: Cooperative mapping of multiple PTZ cameras in automated surveillance systems. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR (2009)

  18. Lowe D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 20, 91–110 (2003)

    Google Scholar 

  19. Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, Portugal (Feb 2009)

  20. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography Tech report 213, AI Center, SRI International (1980)

  21. McLauchlan P., Jaenicke A.: Image mosaicing using sequential bundle adjustment. Image Vis. Comput. 20(9–10), 751–759 (2002)

    Google Scholar 

  22. Dempster A., Laird N., Rubin D.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B (Methodological) 39(1), 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  23. KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Proc. European Workshop Advanced Video Based Surveillance Systems (2001)

  24. Mikolajczyk K., Schmid C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)

    Article  Google Scholar 

  25. Bookstein F.L.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11(6), 567–585 (1989)

    Article  MATH  Google Scholar 

  26. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features European Conference on Computer Vision, pp. 7–13. Graz, Austria May 2006

  27. Chui, H., Rangarajan, A.: A new algorithm for non-rigid point matching. In: IEEE Conference on ComputerVision and Pattern Recognition (CVPR), vol. 2, pp. 44–51 (2000)

  28. Capel, D., Zisserman, A.: Automated mosaicing with super-resolution zoom. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 885–891, Santa Barbara (1998)

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Authors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Kang Xue, Yue Liu, Jing Chen & Jiangen Zhang

  2. Georgia Institute of Technology, Atlanta, GA, USA

    Kang Xue & Gbolabo Ogunmakin

Authors
  1. Kang Xue
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  2. Yue Liu
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  3. Gbolabo Ogunmakin
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  4. Jing Chen
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  5. Jiangen Zhang
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Corresponding author

Correspondence to Kang Xue.

Additional information

A short version has been accepted by ICIP2011.

Scale in our paper is defined as the zoom ratio of the PTZ camera which could be found in Eq. 22, Sect. 6.2.

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Xue, K., Liu, Y., Ogunmakin, G. et al. Panoramic Gaussian Mixture Model and large-scale range background substraction method for PTZ camera-based surveillance systems. Machine Vision and Applications 24, 477–492 (2013). https://doi.org/10.1007/s00138-012-0426-4

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  • DOI: https://doi.org/10.1007/s00138-012-0426-4

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