Disparity Statistics for Pedestrian Detection: Combining Appearance, Motion and Stereo

  • Stefan Walk
  • Konrad Schindler
  • Bernt Schiele
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6316)


Pedestrian detection is an important problem in computer vision due to its importance for applications such as visual surveillance, robotics, and automotive safety. This paper pushes the state-of-the-art of pedestrian detection in two ways. First, we propose a simple yet highly effective novel feature based on binocular disparity, outperforming previously proposed stereo features. Second, we show that the combination of different classifiers often improves performance even when classifiers are based on the same feature or feature combination. These two extensions result in significantly improved performance over the state-of-the-art on two challenging datasets.


Ground Plane Motion Information Human Detection Pedestrian Detection Disparity Statistics 
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.
    Gavrila, D.M., Munder, S.: Multi-cue pedestrian detection and tracking from a moving vehicle. IJCV 73, 41–59 (2007)CrossRefGoogle Scholar
  2. 2.
    Ess, A., Leibe, B., Schindler, K., van Gool, L.: A mobile vision system for robust multi-person tracking. In: CVPR (2008)Google Scholar
  3. 3.
    Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Wojek, C., Walk, S., Schiele, B.: Multi-cue onboard pedestrian detection. In: CVPR (2009)Google Scholar
  5. 5.
    Papageorgiou, C., Poggio, T.: A trainable system for object detection. IJCV 38, 15–33 (2000)zbMATHCrossRefGoogle Scholar
  6. 6.
    Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: ICCV (2003)Google Scholar
  7. 7.
    Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. In: PAMI (2009)Google Scholar
  8. 8.
    Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: A benchmark. In: CVPR (2009)Google Scholar
  9. 9.
    Andriluka, M., Roth, S., Schiele, B.: Pictorial structures revisited: People detection and articulated pose estimation. In: CVPR (2009)Google Scholar
  10. 10.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)Google Scholar
  11. 11.
    Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: CVPR (2008)Google Scholar
  12. 12.
    Wang, X., Han, T.X., Yan, S.: A HOG-LBP human detector with partial occlusion handling. In: ICCV (2009)Google Scholar
  13. 13.
    Dalal, N.: Finding People in Images and Videos. PhD thesis, Institut National Polytechnique de Grenoble (2006)Google Scholar
  14. 14.
    Rohrbach, M., Enzweiler, M., Gavrila, D.M.: High-level fusion of depth and intensity for pedestrian classification. In: Denzler, J., Notni, G., Süße, H. (eds.) DAGM 2009. LNCS, vol. 5748, pp. 101–110. Springer, Heidelberg (2009)Google Scholar
  15. 15.
    Rapus, M., Munder, S., Baratoff, G., Denzler, J.: Pedestrian recognition using combined low-resolution depth and intensity images. In: IEEE Intelligent Vehicles Symposium (2008)Google Scholar
  16. 16.
    Shashua, A., Gdalyahu, Y., Hayun, G.: Pedestrian detection for driving assistance systems: Single-frame classification and system level performance. In: IVS (2004)Google Scholar
  17. 17.
    Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: CVPR (2007)Google Scholar
  18. 18.
    Lin, Z., Davis, L.S.: A pose-invariant descriptor for human detection and segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 423–436. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Zhu, Q., Yeh, M.C., Cheng, K.T., Avidan, S.: Fast human detection using a cascade of histograms of oriented gradients. In: CVPR (2006)Google Scholar
  20. 20.
    Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet part detectors. IJCV 75, 247–266 (2007)CrossRefGoogle Scholar
  21. 21.
    Duin, R.P.W., Tax, D.M.J.: Experiments with classifier combining rules. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, p. 16. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  22. 22.
    Ess, A., Leibe, B., Schindler, K., van Gool, L.: Moving obstacle detection in highly dynamic scenes. In: ICRA (2009)Google Scholar
  23. 23.
    Ess, A., Leibe, B., Schindler, K., Van Gool, L.: Robust multi-person tracking from a mobile platform. PAMI 31(10), 1831–1846 (2009)Google Scholar
  24. 24.
    Babenko, B., Dollár, P., Tu, Z., Belongie, S.: Simultaneous learning and alignment: Multi-instance and multi-pose learning. In: ECCV workshop on Faces in Real-Life Images (2008)Google Scholar
  25. 25.
    Kim, T.K., Cipolla, R.: MCBoost: Multiple classifier boosting for perceptual co-clustering of images and visual features. In: NIPS (2008)Google Scholar
  26. 26.
    Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., Bischof, H.: Anisotropic Huber-L1 optical flow. In: BMVC (2009)Google Scholar
  27. 27.
    Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: CVPR (2008)Google Scholar
  28. 28.
    Gehler, P.V., Nowozin, S.: On feature combination for multiclass object classification. In: ICCV (2009)Google Scholar
  29. 29.
    Zach, C., Frahm, J.M., Niethammer, M.: Continuous maximal flows and Wulff shapes: Application to MRFs. In: CVPR (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Stefan Walk
    • 1
  • Konrad Schindler
    • 1
    • 2
  • Bernt Schiele
    • 1
    • 3
  1. 1.Computer Science DepartmentTU Darmstadt 
  2. 2.Photogrammetry and Remote Sensing GroupETH Zürich 
  3. 3.MPI InformaticsSaarbrücken

Personalised recommendations