Posterior Probability Based Multi-classifier Fusion in Pedestrian Detection

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 238)


This paper presents a novel method for pedestrian detection at measurement level. At feature extraction stage, we use Histogram of Oriented Gradient to describe the feature of pedestrian and non-pedestrian. To decrease the time cost, we reduce the dimension by using PCA. The base classifiers used in posterior probability based multi-classifier fusion are posterior probability based SVM, Naïve Bayesian and Minimum Distance Classifier, respectively. To estimate the accuracy of fusion result, stratified cross-validation is used. Experimental results on pedestrian databases prove the efficiency of this work.


pedestrian detection multi-classifier fusion posterior probability stratified cross-validation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gavrila, D.M., Munder, S.: Multi-Cue Pedestrian Detection and Tracking from a Moving Vehicle. International Journal of Computer Vision 73(1), 41–59 (2007)CrossRefGoogle Scholar
  2. 2.
    Parra, I., Fernandez, D., Bergasa, M.A.: Combination of Feature Extraction Methods for SVM Pedestrian Detection. IEEE Trans. on Intelligent Transportation Systems 73(1), 292–307 (2007)Google Scholar
  3. 3.
    Xu, L., Krzyzak, A., Suen, C.Y.: Methods of Combining Multiple Classifiers and their Application to Hand Writing Recognition. IEEE Trans. on Systems, Man, and Cybernetics 22, 418–435 (1992)CrossRefGoogle Scholar
  4. 4.
    Ng, K.C., Abramson, B.: Consensus diagnosis: A simulation study. IEEE Trans. on Systems, Man, and Cybernetics 22, 916–928 (1992)CrossRefGoogle Scholar
  5. 5.
    Duin, R.: The Combining Classifier: to Train or not to Train? In: Proceedings of the International Conference on Pattern Recognition, vol. 2, pp. 765–770 (2002)Google Scholar
  6. 6.
    Duin, R.P.W., Tax, D.M.J.: Experiments with Classifier Combining Rules. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 16–29. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Dass, S.C., Nandarumar, K., Jain, A.K.: A Principled Approach to Score Level Fusion in Multimodal Biometric Systems. In: Proceedings of International Conference on Audio and Video Based Biometric Person Authentication, pp. 1049–1058 (2005)Google Scholar
  8. 8.
    Lee, D.S., Srihari, S.N.: A Theory of Classifier Combination: the Neural Network Approach. In: The 3rd International Conference on Document Analysis and Recognition, pp. 42–45 (1995)Google Scholar
  9. 9.
    Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery Data Mining 2(2), 1–43 (1998)Google Scholar
  10. 10.
    Al-Ani, A., Deriche, M.: A New Technique for Combining Multiple Classifiers Using the Demspter Shafer Theory of Evidence 17, 333–361 (2002)Google Scholar
  11. 11.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  12. 12.
    Wang, Z.R., Jia, U.L., Huang, H., Tang, S.M.: Pedestrian Detection Using Boosted HOG Features. In: Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, pp. 1155–1160 (2008)Google Scholar
  13. 13.
    John, C.P.: Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. Advances in Large Margin Classifiers, 61–73 (1999)Google Scholar
  14. 14.
    Jain, A.K., Duin, R., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)CrossRefGoogle Scholar
  15. 15.
    Kuncheva, L.I., Bezdek, J.C., Duin, R.P.: Decision Templates for Multiple Classifier Fusion: An Experimental Comparison. Pattern Recognition 34(2), 299–314 (2001)CrossRefMATHGoogle Scholar
  16. 16.
    Kuncheva, L.: Combining Pattern Classifiers: methods and algorithms, p. 124. Wiley (2004)Google Scholar
  17. 17.
    Gerónimo, O., Sappa, A.D., López, A., Ponsa, D.: Adaptive Image Sampling and Windows Classification for on-board Pedestrian Detection. In: Proceedings of the International Conference on Computer Vision Systems, Bielefeld, Germany (2007)Google Scholar
  18. 18.
    Munder, S., Gavrila, D.M.: An Experimental Study on Pedestrian Classification. IEEE Trans. on Pattern Analysis and Machine Intelligence (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Shenzhen Graduate SchoolHarbin Institute of TechnologyShenzhenChina

Personalised recommendations