Multi-camera Tracking and Segmentation of Occluded People on Ground Plane Using Search-Guided Particle Filtering

  • Kyungnam Kim
  • Larry S. Davis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)


A multi-view multi-hypothesis approach to segmenting and tracking multiple (possibly occluded) persons on a ground plane is proposed. During tracking, several iterations of segmentation are performed using information from human appearance models and ground plane homography. To more precisely locate the ground location of a person, all center vertical axes of the person across views are mapped to the top-view plane and their intersection point on the ground is estimated. To tackle the explosive state space due to multiple targets and views, iterative segmentation-searching is incorporated into a particle filtering framework. By searching for people’s ground point locations from segmentations, a set of a few good particles can be identified, resulting in low computational cost. In addition, even if all the particles are away from the true ground point, some of them move towards the true one through the iterated process as long as they are located nearby. We demonstrate the performance of the approach on several video sequences.


Ground Plane Color Model Appearance Model Camera View Multiple Camera 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tu, Z., Zhu, S.-C.: Image segmentation by data-driven Markov chain Monte Carlo. IEEE Transactions on PAMI, 24(5) (May 2002) Google Scholar
  2. 2.
    Javed, O., Shafique, K., Shah, M.: Appearance Modeling for Tracking in Multiple Non-overlapping Cameras. In: IEEE CVPR (2005)Google Scholar
  3. 3.
    Senior, A.W.: Tracking with Probabilistic Appearance Models. In: ECCV workshop on Performance Evaluation of Tracking and Surveillance Systems, pp. 48–55 (2002)Google Scholar
  4. 4.
    Haritaoglu, I., Harwood, D., Davis, L.S.: W4: real-time surveillance of people and their activities. IEEE Transactions on PAMI 22(8) (August 2000)Google Scholar
  5. 5.
    Chang, T.H., Gong, S., Ong, E.J.: Tracking Multiple People Under Occlusion Using Multiple Cameras. BMVC (2000)Google Scholar
  6. 6.
    Kang, J., Cohen, I., Medioni, G.: Multi-Views Tracking Within and Across Uncalibrated Camera Streams. In: ACM SIGMM 2003 Workshop on Video Surveillance (2003)Google Scholar
  7. 7.
    Javed, O., Rasheed, Z., Shafique, K., Shah, M.: Tracking Across Multiple Cameras With Disjoint Views. In: The Ninth IEEE ICCV (2003)Google Scholar
  8. 8.
    Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Trans. on PAMI 24(5), 603–619 (2002)Google Scholar
  9. 9.
    Zhao, T., Nevatia, R.: Bayesian human segmentation in crowded situations. In: CVPR (June 2003)Google Scholar
  10. 10.
    Stauffer, C., Tieu, K.: Automated multi-camera planar tracking correspondence modeling. CVPR 01(1), 259 (2003)Google Scholar
  11. 11.
    Hu, M., Lou, J., Hu, W., Tan, T.: Multicamera correspondence based on principal axis of human body. In: IEEE ICIP (2004)Google Scholar
  12. 12.
    Arulampalam, S., Maskell, S., Gordon, N.J., Clapp, T.: A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking. IEEE Transactions of Signal Processing 50(2), 174–188 (2002)CrossRefGoogle Scholar
  13. 13.
    Deutscher, J., Blake, A., Reid, I.: Articulated Body Motion Capture by Annealed Particle Filtering. In: CVPR (2000)Google Scholar
  14. 14.
    Sullivan, J., Rittscher, J.: Guiding random particles by deterministic search. In: ICCV (2001)Google Scholar
  15. 15.
    Shan, C., Wei, Y., Tan, T., Ojardias, F.: Real Time Hand Tracking by Combining Particle Filtering and Mean Shift. In: IEEE International Conference on Automatic Face and Gesture Recognition (2004)Google Scholar
  16. 16.
    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
  17. 17.
    Isard, M., Blake, A.: CONDENSATION – conditional density propagation for visual tracking. Int. J. Computer Vision 29(1), 5–28 (1998)CrossRefGoogle Scholar
  18. 18.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foregroundbackground segmentation using codebook model. In: Real-Time Imaging (June 2005)Google Scholar
  19. 19.
    Mittal, A., Davis, L.S.: M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene. IJCV 51(3) (2003)Google Scholar
  20. 20.
    Elgammal, A., Davis, L.S.: Probabilistic Framework for Segmenting People Under Occlusion. In: ICCV, Vancouver, Canada, July 9-12 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kyungnam Kim
    • 1
    • 2
  • Larry S. Davis
    • 1
  1. 1.Computer Vision LabUniversity of MarylandCollege ParkUSA
  2. 2.IPIX CorporationReston

Personalised recommendations