Advertisement

Pedestrian Detection Using Global-Local Motion Patterns

  • Dhiraj Goel
  • Tsuhan Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)

Abstract

We propose a novel learning strategy called Global-Local Motion Pattern Classification (GLMPC) to localize pedestrian-like motion patterns in videos. Instead of modeling such patterns as a single class that alone can lead to high intra-class variability, three meaningful partitions are considered - left, right and frontal motion. An AdaBoost classifier based on the most discriminative eigenflow weak classifiers is learnt for each of these subsets separately. Furthermore, a linear three-class SVM classifier is trained to estimate the global motion direction. To detect pedestrians in a given image sequence, the candidate optical flow sub-windows are tested by estimating the global motion direction followed by feeding to the matched AdaBoost classifier. The comparison with two baseline algorithms including the degenerate case of a single motion class shows an improvement of 37% in false positive rate.

Keywords

Support Vector Machine Motion Pattern Global Motion Frontal Motion Linear Support Vector Machine 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., Poggio, T.: Pedestrian detection using wavelet templates. CVPR, 193–199 (1997)Google Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. CVPR 1, 886–893 (2005)Google Scholar
  3. 3.
    Viola, P., Jones, M., Snow, D.: Detecting Pedestrians Using Patterns of Motion and Appearance. ICCV 2, 734–741 (2003)Google Scholar
  4. 4.
    Fablet, R., Black, M.J.: Automatic Detection and Tracking of Human Motion with a View-Based Representation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 476–491. Springer, Heidelberg (2002)Google Scholar
  5. 5.
    Sidenbladh, H.: Detecting Human Motion with Support Vector Machines. ICPR 2, 188–191 (2004)Google Scholar
  6. 6.
    Goel, D., Chen, T.: Real-time Pedestrian Detection using Eigenflow. In: IEEE International Conference on Image Processing, IEEE Computer Society Press, Los Alamitos (2007)Google Scholar
  7. 7.
  8. 8.
    Bruhn, A., Weickert, J., Schnörr, C.: Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods. IJCV 61, 211–231 (2005)CrossRefGoogle Scholar
  9. 9.
    Bruhn, A., Weickert, J., Kohlberger, T., Schnörr, C.: A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods. IJCV 69, 257–277 (2006)CrossRefGoogle Scholar
  10. 10.
    Liu, X., Chen, T., Kumar, B.V.: Face authentication for multiple subjects using eigenflow. Pattern Recognition 36, 313–328 (2003)CrossRefGoogle Scholar
  11. 11.
    Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. CVPR (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Dhiraj Goel
    • 1
  • Tsuhan Chen
    • 1
  1. 1.Department of Electrical and Computer Engineering, Carnegie Mellon UniversityU.S.A.

Personalised recommendations