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Person Detection with a Computation Time Weighted AdaBoost

  • Alhayat Ali Mekonnen
  • Frédéric Lerasle
  • Ariane Herbulot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)

Abstract

In this paper, a boosted cascade person detection framework with heterogeneous pool of features is presented. The boosted cascade construction and feature selection is carried out using a modified AdaBoost that takes computation time of features into consideration. The final detector achieves a low Miss Rate of 0.06 at 10− 3 False Positive Per Window on the INRIA public dataset while achieving an average speed up of 1.8× on the classical variant.

Keywords

Person Detection AdaBoost Feature Selection 

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References

  1. 1.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. CVPR, pp. 886–893 (2005)Google Scholar
  2. 2.
    Dollar, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: Proc. BMVC, pp. 1–11 (2009)Google Scholar
  3. 3.
    Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: An evaluation of the state of the art. IEEE T-PAMI 34(4), 743–761 (2012)CrossRefGoogle Scholar
  4. 4.
    Gerónimo, D., López, A., Ponsa, D., Sappa, A.D.: Haar wavelets and edge orientation histograms for on–board pedestrian detection. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4477, pp. 418–425. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Geronimo, D., Lopez, A., Sappa, A., Graf, T.: Survey of pedestrian detection for advanced driver assistance systems. IEEE T-PAMI 32(7), 1239–1258 (2010)CrossRefGoogle Scholar
  6. 6.
    Heikkil, M., Pietikinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognition 42(3), 425–436 (2009)CrossRefGoogle Scholar
  7. 7.
    Hussain, S., Triggs, B.: Feature sets and dimensionality reduction for visual object detection. In: Proc. BMVC, pp. 1–10 (2010)Google Scholar
  8. 8.
    Jourdheuil, L., Allezard, N., Chateau, T., Chesnais, T.: Heterogeneous adaboost with real-time constraints - application to the detection of pedestrians by stereovision. In: Proc. VISAPP, pp. 539–546 (2012)Google Scholar
  9. 9.
    Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: Proc. ICIP, pp. 900–903 (2002)Google Scholar
  10. 10.
    Ojala, T., Pietikinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29(1), 51–59 (1996)CrossRefGoogle Scholar
  11. 11.
    Papageorgiou, C., Poggio, T.: A trainable system for object detection. IJCV 38(1), 15–33 (2000)CrossRefzbMATHGoogle Scholar
  12. 12.
    Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)Google Scholar
  13. 13.
    Schwartz, W.R., Kembhavi, A., Harwood, D., Davis, L.S.: Human detection using partial least squares analysis. In: Proc. ICCV, pp. 24–31 (2009)Google Scholar
  14. 14.
    Viola, P.A., Jones, M.J.: Robust real-time face detection. IJCV 57(2), 137–154 (2004)CrossRefGoogle Scholar
  15. 15.
    Viola, P.A., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: Proc. ICCV, pp. 734–741 (2003)Google Scholar
  16. 16.
    Walk, S., Majer, N., Schindler, K., Schiele, B.: New features and insights for pedestrian detection. In: Proc. CVPR, pp. 1030–1037 (2010)Google Scholar
  17. 17.
    Wang, X., Han, T., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: Proc. ICCV, pp. 32–39 (2009)Google Scholar
  18. 18.
    Wu, B., Nevatia, R.: Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection. In: Proc. CVPR, pp. 1–8 (2008)Google Scholar
  19. 19.
    Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE T-PAMI 29(6), 915–928 (2007)CrossRefGoogle Scholar
  20. 20.
    Zhu, Q., Yeh, M.C., Cheng, K.T., Avidan, S.: Fast human detection using a cascade of histograms of oriented gradients. In: Proc. CVPR, pp. 1491–1498 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alhayat Ali Mekonnen
    • 1
    • 2
  • Frédéric Lerasle
    • 1
    • 2
  • Ariane Herbulot
    • 1
    • 2
  1. 1.CNRS, LAASToulouseFrance
  2. 2.Univ de Toulouse, UPS, LAASToulouseFrance

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