Tracking by Cluster Analysis of Feature Points and Multiple Particle Filters

  • Wei Du
  • Justus Piater
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3687)

Abstract

A moving target produces a coherent cluster of feature points in the image plane. This motivates our novel method of tracking multiple targets by cluster analysis of feature points and multiple particle filters. First, feature points are detected by a Harris corner detector and tracked by a Lucas-Kanade tracker. Clusters of moving targets are then initialized by grouping spatially co-located points with similar motion using the EM algorithm. Due to the non-Gaussian distribution of the points in a cluster and the multi-modality resulting from multiple targets, multiple particle filters are applied to track all the clusters simultaneously: one particle filter is started for one cluster. The proposed method is well suited for the typical video surveillance configuration where the cameras are still and targets of interest appear relatively small in the image. We demonstrate the effectiveness of our method on different PETS datasets.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Harris, C., Stephens, M.: A Combined Corner and Edge Detector. In: Fourth Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  2. 2.
    Lucas, D.B., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)Google Scholar
  3. 3.
    Arnaud, E., Memin, E., Cernuschi-Frias, B.: A Robust Stochastic Filter for Point Tracking in Image Sequences. In: Asian Conference on Computer Vision, Korea (2004)Google Scholar
  4. 4.
    Shafique, K., Shah, M.: A Noniterative Greedy Algorithm for Multiframe Point Correspondence. IEEE Transactions on PAMI 27(1), 51–65 (2005)Google Scholar
  5. 5.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society 39(1), 1–38 (1977)MATHMathSciNetGoogle Scholar
  6. 6.
    Lookingbill, A., Lieb, D., Stavens, D., Thrun, S.: Learning Activity-based Ground Models from a Moving Helicopter Platform. In: ICRA, Spain (2005)Google Scholar
  7. 7.
    Ferryman, J.: PETS (2001), Datasets http://visualsurveillance.org/PETS2001
  8. 8.
    Pece, A.E.C.: Generative-Model-Based Tracking by Cluster Analysis of Image Differences. Robotics and Autonomous Systems 49(3), 181–194 (2002)CrossRefGoogle Scholar
  9. 9.
    Du, W., Piater, J., Verly, J.: Tracking by Perceptually Grouping Feature Points into Clusters (submitted)Google Scholar
  10. 10.
    Doucet, A., Freitas, N., Godor, N. (eds.): Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2000)Google Scholar
  11. 11.
    Medioni, G., Tang, C.K.: Inference of Integrated Surface, Curve and Junction Descriptions from Sparse 3-D Data. IEEE Transactions on PAMI 20(11), 1206–1223 (1998)Google Scholar
  12. 12.
    Vermaak, J., Doucet, A., Perez, P.: Maintaining Multi-Modality through Mixture Tracking. In: International Conference on Computer Vision, Nice, France (2003)Google Scholar
  13. 13.
    Okuma, K., Taleghani, A., Freitas, N.D., Little, J.J., Lowe, D.G.: A Boosted Particle Filter: Multitarget Detection and Tracking. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 28–39. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Piater, J., Crowley, J.: Multi-Modal Tracking of Interacting Targets Using Gaussian Ap¬proximations. In: Proceedings of the Second IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, Hawaii, USA (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Wei Du
    • 1
  • Justus Piater
    • 1
  1. 1.Department of Electrical Engineering and Computer Science, Institut MontefioreUniversity of LiègeLiègeBelgium

Personalised recommendations