A Fast Method for Tracking People with Multiple Cameras

  • Alparslan Yildiz
  • Yusuf Sinan Akgul
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6553)


We propose a multi-camera method to track several persons using constraints from the epipolar and projective geometries. The method is very accurate, fast, and simple. We first compute accumulator images for each time frame that shows the probability of object positions on the ground. We developed a voting based method that allows employment of the integral images to make the accumulator computation very fast. Next, we perform two-pass 3D tracking on the volume generated by stacking these accumulator images. Our main contributions are the fast computation of the accumulator images and application of fast 3D tracking methods like the Kalman Smoother instead of the computationally expensive methods like the Viterbi algorithm.

The proposed tracking method is evaluated on people videos captured using four synchronized cameras.


Ground Plane Object Position Integral Image Multiple Camera Epipolar Geometry 
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.


  1. 1.
    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, Part III. LNCS, vol. 3953, pp. 98–109. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    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, Part IV. LNCS, vol. 3954, pp. 133–146. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Lee, L., Romano, R., Stein, G.: Monitoring activities from multiple video streams: establishing a common coordinate frame. IEEE Transactions on PAMI 22(8), 758–767 (2000)CrossRefGoogle Scholar
  4. 4.
    Black, J., Ellis, T., Rosin, P.: Multi view image surveillance and tracking. In: Proceedings of the Workshop on Motion and Video Computing, December 5-6, pp. 169–174 (2002)Google Scholar
  5. 5.
    Chang, T., Gong, S.: Tracking Multiple People with a Multi-Camera System. In: IEEE Workshop on Multi-Object Tracking, WOMOT 2001 (2001)Google Scholar
  6. 6.
    Khan, S.M., Shah, M.: Tracking Multiple Occluding People by Localizing on Multiple Scene Planes. PAMI (2009)Google Scholar
  7. 7.
    Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multicamera People Tracking with a Probabilistic Occupancy Map. PAMI (2008)Google Scholar
  8. 8.
    Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: CVPR (2001)Google Scholar
  9. 9.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV (2004)Google Scholar
  10. 10.
    Crimisini, A., Reid, I., Zisserman, A.: Single View Metrology. In: ICCV (1999)Google Scholar
  11. 11.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge Univ. Press (2002)Google Scholar
  12. 12.
    Andriluka, M., Roth, S., Schiele, B.: Monocular 3D Pose Estimation and Tracking by Detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alparslan Yildiz
    • 1
  • Yusuf Sinan Akgul
    • 1
  1. 1.Vision Lab. Gebze Institute of TechnologyTurkey

Personalised recommendations