Object Detection and Tracking for Intelligent Video Surveillance

  • Kyungnam Kim
  • Larry S. Davis
Part of the Studies in Computational Intelligence book series (SCI, volume 346)

Abstract

As CCTV/IP cameras and network infrastructure become cheaper and more affordable, today’s video surveillance solutions are more effective than ever before, providing new surveillance technology that’s applicable to a wide range end-users in retail sectors, schools, homes, office campuses, industrial /transportation systems, and government sectors. Vision-based object detection and tracking, especially for video surveillance applications, is studied from algorithms to performance evaluation. This chapter is composed of three topics: (1) background modeling and detection, (2) performance evaluation of sensitive target detection, and (3) multi-camera segmentation and tracking of people.

Keywords

video surveillance object detection and tracking background subtraction performance evaluation multi-view people tracking CCTV/IP cameras 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for real-time robust background subtraction and shadow detection. In: IEEE Frame-Rate Applications Workshop, Kerkyra, Greece (1999)Google Scholar
  2. 2.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Int. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)Google Scholar
  3. 3.
    Harville, M.: A framework for high-level feedback to adaptive, per-pixel, mixture-of-gaussian background models. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 543–560. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751–767. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Kohonen, T.: Learning vector quantization. Neural Networks 1, 3–16 (1988)CrossRefGoogle Scholar
  6. 6.
    Chalidabhongse, T.H., Kim, K., Harwood, D., Davis, L.: A Perturbation Method for Evaluating Background Subtraction Algorithms. In: Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS (2003)Google Scholar
  7. 7.
    Scotti, G., Marcenaro, L., Regazzoni, C.: A S.O.M. based algorithm for video surveillance system parameter optimal selection. In: IEEE Conference on Advanced Video and Signal Based Surveillance (2003)Google Scholar
  8. 8.
    Haritaoglu, I., Harwood, D., Davis, L.S.: W 4: real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 809–830 (2000)CrossRefGoogle Scholar
  9. 9.
    Elgammal, A., Davis, L.S.: Probabilistic Framework for Segmenting People Under Occlusion. In: IEEE International Conference on Computer Vision, Vancouver, Canada, July 9-12 (2001)Google Scholar
  10. 10.
    Zhao, T., Nevatia, R.: Tracking Multiple Humans in Complex Situations. IEEE Trans. Pattern Analysis Machine Intell. 26(9) (September 2004)Google Scholar
  11. 11.
    Rabaud, V., Belongie, S.: Counting Crowded Moving Objects. In: IEEE Conf. on Comp. Vis. and Pat. Rec. (2006)Google Scholar
  12. 12.
    Yang, D., Gonzalez-Banos, H., Guibas, L.: Counting People in Crowds with a Real-Time Network of Image Sensors. In: IEEE ICCV (2003)Google Scholar
  13. 13.
    Khan, S.M., Shah, M.: A Multiview Approach to Tracking People in Crowded Scenes Using a Planar Homography Constraint. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 133–146. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Kang, J., Cohen, I., Medioni, G.: Multi-Views Tracking Within and Across Uncalibrated Camera Streams. In: Proceedings of the ACM SIGMM 2003 Workshop on Video Surveillance (2003)Google Scholar
  15. 15.
    Javed, O., Rasheed, Z., Shafique, K., Shah, M.: Tracking Across Multiple Cameras With Disjoint Views. In: The Ninth IEEE International Conference on Computer Vision, Nice, France (2003)Google Scholar
  16. 16.
    Mittal, A., Davis, L.S.: M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene. International Journal of Computer Vision 51(3) (February/March 2003)Google Scholar
  17. 17.
    Eshel, R., Moses, Y.: Homography Based Multiple Camera Detection and Tracking of People in a Dense Crowd. In: Computer Vision and Pattern Recognition, CVPR (2008)Google Scholar
  18. 18.
    Jin, H., Qian, G., Birchfield, D.: Real-Time Multi-View Object Tracking in Mediated Environments. In: ACM Multimedia Modeling Conference (2008)Google Scholar
  19. 19.
    Black, J., Ellis, T.: Multi Camera Image Tracking. In: 2nd IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (2001)Google Scholar
  20. 20.
    Xu, M., Orwell, J., Jones, G.A.: Tracking football players with multiple cameras. In: ICIP 2004 (2004)Google Scholar
  21. 21.
    Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multi-Camera People Tracking with a Probabilistic Occupancy Map. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), 267–282 (2008)CrossRefGoogle Scholar
  22. 22.
    Tsai, R.Y.: An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision. In: IEEE Conference on Computer Vision and Pattern Recognition (1986)Google Scholar
  23. 23.
    Tu, Z., Zhu, S.-C.: Image segmentation by data-driven Markov chain Monte Carlo. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 657–673 (2002)CrossRefGoogle Scholar
  24. 24.
    Javed, O., Shafique, K., Shah, M.: Appearance Modeling for Tracking in Multiple Non-overlapping Cameras. In: IEEE CVPR 2005, San Diego, June 20-26 (2005)Google Scholar
  25. 25.
    Senior, A.W.: Tracking with Probabilistic Appearance Models. In: Proceedings ECCV workshop on Performance Evaluation of Tracking and Surveillance Systems, June 1, pp. 48–55 (2002)Google Scholar
  26. 26.
    Chang, T.H., Gong, S., Ong, E.J.: Tracking Multiple People Under Occlusion Using Multiple Cameras. In: BMVC (2000)Google Scholar
  27. 27.
    Perez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  28. 28.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real-Time Imaging 11(3), 172–185 (2005)CrossRefGoogle Scholar
  29. 29.
    Hu, M., Lou, J., Hu, W., Tan, T.: Multicamera correspondence based on principal axis of human body. In: International Conference on Image Processing (2004)Google Scholar
  30. 30.
    Kim, K., Davis, L.S.: Multi-camera tracking and segmentation of occluded people on ground plane using search-guided particle filtering. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 98–109. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kyungnam Kim
    • 1
  • Larry S. Davis
    • 2
  1. 1.HRL LaboratoriesLLC.MalibuUSA
  2. 2.Computer Science Dept.University of MarylandCollege ParkUSA

Personalised recommendations