Cascaded Confidence Filtering for Improved Tracking-by-Detection

  • Severin Stalder
  • Helmut Grabner
  • Luc Van Gool
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)


We propose a novel approach to increase the robustness of object detection algorithms in surveillance scenarios. The cascaded confidence filter successively incorporates constraints on the size of the objects, on the preponderance of the background and on the smoothness of trajectories. In fact, the continuous detection confidence scores are analyzed locally to adapt the generic detector to the specific scene. The approach does not learn specific object models, reason about complete trajectories or scene structure, nor use multiple cameras. Therefore, it can serve as preprocessing step to robustify many tracking-by-detection algorithms. Our real-world experiments show significant improvements, especially in the case of partial occlusions, changing backgrounds, and similar distractors.


Object Detection Ground Plane Background Model Human Detection Scene Structure 
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.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. CVPR, vol. I, pp. 511–518 (2001)Google Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. CVPR, vol. 1, pp. 886–893 (2005)Google Scholar
  3. 3.
    Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proc. CVPR (2008)Google Scholar
  4. 4.
    Hoiem, D., Efros, A.A., Hebert, M.: Putting objects in perspective. In: Proc. CVPR., vol. 2, pp. 2137–2144 (2006)Google Scholar
  5. 5.
    Li, Y., Wu, B., Nevatia, R.: Human detection by searching in 3d space using camera and scene knowledge. In: Proc. ICPR (2008)Google Scholar
  6. 6.
    Roth, P., Sternig, S., Grabner, H., Bischof, H.: Classifier grids for robust adaptive object detection. In: Proc. CVPR (2009)Google Scholar
  7. 7.
    Stalder, S., Grabner, H., Gool, L.V.: Exploring context to learn scene specifc object detectors. In: Proc. PETS (2009)Google Scholar
  8. 8.
    Leibe, B., Schindler, K., Gool, L.V.: Coupled detection and trajectory estimation for multi-object tracking. In: Proc. ICCV (2007)Google Scholar
  9. 9.
    Huang, C., Wu, B., Nevatia, R.: Robust object tracking by hierarchical association of detection responses. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 788–801. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Breitenstein, M., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: Robust tracking-by-detection using a detector confidence particle filter. In: Proc. ICCV (2009)Google Scholar
  11. 11.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proc. CVPR, vol. II, pp. 246–252 (1999)Google Scholar
  12. 12.
    Frangi, A., Niessen, W., Vincken, K., Viergever, M.: Multiscale vessel enhancement filtering, pp. 130–137 (1998)Google Scholar
  13. 13.
    Florin, C., Paragios, N., Williams, J.: Particle filters, a quasi-monte carlo solution for segmentation of coronaries. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 246–253. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge (VOC 2009) Results (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Severin Stalder
    • 1
  • Helmut Grabner
    • 1
  • Luc Van Gool
    • 1
    • 2
  1. 1.Computer Vision LaboratoryETH ZurichSwitzerland
  2. 2.ESAT - PSI / IBBTK.U. LeuvenBelgium

Personalised recommendations