Fast and Accurate Pedestrian Detection Using a Cascade of Multiple Features

  • Alaa Leithy
  • Mohamed N. Moustafa
  • Ayman Wahba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)


We propose a fast and accurate pedestrian detection framework based on cascaded classifiers with two complementary features. Our pipeline starts with a cascade of weak classifiers using Haar-like features followed by a linear SVM classifier relying on the Co-occurrence Histograms of Oriented Gradients (CoHOG). CoHOG descriptors have a strong classification capability but are extremely high dimensional. On the other hand, Haar features are computationally efficient but not highly discriminative for extremely varying texture and shape information such as pedestrians with different clothing and stances. Therefore, the combination of both classifiers enables fast and accurate pedestrian detection. Additionally, we propose reducing CoHOG descriptor dimensionality using Principle Component Analysis. The experimental results on the DaimlerChrysler benchmark dataset show that we can reach very close accuracy to the CoHOG-only classifier but in less than 1/1000 of its computational cost.


Support Vector Machine Principle Component Analysis Linear Support Vector Machine Oriented Gradient Pedestrian Detection 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cortes, C., Vapnik, V.: Support-vector networks. J. of Machine Learning 20(3), 273–297 (1995)zbMATHGoogle Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)Google Scholar
  3. 3.
    Dollar, P., Tu, Z., Tao, H., Belongie, S.: Feature mining for image classification. In: CVPR, pp. 1–8 (2007)Google Scholar
  4. 4.
    Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: A library for large linear classification. J. of Machine Learning Research 9, 1871–1874 (2008)zbMATHGoogle Scholar
  5. 5.
    Gavrila, D.M., Munder, S.: An experimental study on pedestrian classification. IEEE Tran. of PAMI 28(11), 1863–1868 (2006)CrossRefGoogle Scholar
  6. 6.
    Gernimo, D., Lpez, A.M., Sappa, A.D., Graf, T.: Survey of Pedestrian Detection for Advanced Driver Assitance Systems. IEEE Tran. of PAMI 32(7), 1239–1258 (2010)CrossRefGoogle Scholar
  7. 7.
    Kozakaya, T., Ito, S., Kubota, S., Yamaguchi, O.: Cat Face Detection with Two Heterogeneous Features. In: ICIP, pp. 1213–1216 (2009)Google Scholar
  8. 8.
    Lin, Z., Davis, L.S.: Shape-Based Human Detection and Segmentation via Hierarchical Part-Template Matching. IEEE Tran. of PAMI 32(4), 604–618 (2010)CrossRefGoogle Scholar
  9. 9.
    Mita, T., Kaneko, T., Stenger, B., Hori, O.: Discriminative feature co-occurrence selection for object detection. IEEE Tran. of PAMI 30(7), 1257–1269 (2008)CrossRefGoogle Scholar
  10. 10.
    Yamauchi, Y., Fujiyoshi, H., Iwahori, Y., Kanade, T.: People detection based on co-occurrence of appearance and spatio-temporal features. J. of NII Transactions on Progress in Informatics 7, 33–42 (2010)CrossRefGoogle Scholar
  11. 11.
    Viola, P., Jones, M.: Robust real-time face detection. Int. J. of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  12. 12.
    Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: ICCV, pp. 734–741 (2003)Google Scholar
  13. 13.
    Watanabe, T., Ito, S., Yokoi, K.: Co-occurrence histograms of oriented gradients for pedestrian detection. In: Wada, T., Huang, F., Lin, S. (eds.) PSIVT 2009. LNCS, vol. 5414, pp. 37–47. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
  15. 15.
    The PASCAL Visual Object Classes Challenge (VOC), (last visited, June 2010)
  16. 16.
    INRIA Person Dataset: , (last visited, June 2010)

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alaa Leithy
    • 1
  • Mohamed N. Moustafa
    • 1
  • Ayman Wahba
    • 1
  1. 1.Computer Engineering, Faculty of EngineeringAin Shams UniversityCairoEgypt

Personalised recommendations