Advertisement

Journal of Real-Time Image Processing

, Volume 5, Issue 4, pp 245–257 | Cite as

Cascaded online boosting

  • Ingrid Visentini
  • Lauro SnidaroEmail author
  • Gian Luca Foresti
Special Issue

Abstract

In this paper, we propose a cascaded version of the online boosting algorithm to speed-up the execution time and guarantee real-time performance even when employing a large number of classifiers. This is the case for target tracking purposes in computer vision applications. We thus revise the online boosting framework by building on-the-fly a cascade of classifiers dynamically for each new frame. The procedure takes into account both the error and the computational requirements of the available features and populates the levels of the cascade accordingly to optimize the detection rate while retaining real-time performance. We demonstrate the effectiveness of our approach on standard datasets.

Keywords

Online boosting Multiple classifiers systems Object detection Tracking 

References

  1. 1.
    Amit, Y., Geman, D.: A computational model for visual selection. Neural Comput. 11(7), 1691–1715 (1999)CrossRefGoogle Scholar
  2. 2.
    Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal Mach. Intell. 29(2), 261–271 (2007)CrossRefGoogle Scholar
  3. 3.
    Bourdev, L., Brandt, J:. Robust object detection via soft cascade. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, pp. 236–243. IEEE Computer Society, Washington, DC (2005)Google Scholar
  4. 4.
    Charles Brubaker, S., Wu, J., Sun, J., Mullin, M.D., Rehg, J.M.: On the design of cascades of boosted ensembles for face detection. Int. J. Comput. Vis. 77(1–3), 65–86 (2008)CrossRefGoogle Scholar
  5. 5.
    Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1631–1643 (2005)CrossRefGoogle Scholar
  6. 6.
    Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Thirteen International Conference on Machine Learning, pp. 148–156 (1996)Google Scholar
  7. 7.
    Garcfa-Pedrajas, N., Ortiz-Boyer, D., del Castillo-Gomariz, R., Hervs-Martfnez C.: Cascade ensembles. Lect. Notes Comput. Sci. 3512, 598–603 (2005)Google Scholar
  8. 8.
    Gouet-Brunet, V., Lameyre, B.: Object recognition and segmentation in videos by connecting heterogeneous visual features. Comput. Vis. Image Understand. 111(1), 86–109 (2008)CrossRefGoogle Scholar
  9. 9.
    Grabner, H., Sochman, J., Bischof, H., Matas, J.: Training sequential on-line boosting classifier for visual tracking. In: International Conference on Pattern Recognition (2008)Google Scholar
  10. 10.
    Grabner, H., Bischof, H.: On-line boosting and vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 260–267, Los Alamitos, CA, USA (2006)Google Scholar
  11. 11.
    Hu, W., Hu, W., Maybank, S.: Adaboost-based algorithm for network intrusion detection. IEEE Trans. Syst. Man Cybern. B 38(2), 577–583 (2008)CrossRefGoogle Scholar
  12. 12.
    Islam, M.M., Yao, X., Nirjon, S.M.S., Islam, M.A., Murase, K.: Bagging and boosting negatively correlated neural networks. IEEE Trans. Syst. Man Cybern. B 38(3), 771–784 (2008) CrossRefGoogle Scholar
  13. 13.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)CrossRefGoogle Scholar
  14. 14.
    Lee, S.-W., Kim, S.-Y.: Integrated segmentation and recognition of handwritten numerals with cascade neural network. IEEE Trans. Syst. Man Cybern. C 29(2), 285–290 (1999)CrossRefGoogle Scholar
  15. 15.
    Li, S.Z., Zhang, Z.: Floatboost learning and statistical face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26(9):1112–1123 (2004) CrossRefGoogle Scholar
  16. 16.
    Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. Proc. IEEE Int. Conf. Image Process. 1, 900–903 (2002) CrossRefGoogle Scholar
  17. 17.
    Liu, X.M., Yu, T.: Gradient feature selection for online boosting. In: International Conference on Computer Vision, pp. 1–8 (2007)Google Scholar
  18. 18.
    Masip, D.D., Lapedriza, Á., Vitria, J.J.: Boosted online learning for face recognition. I. Syst. Man Cybern. B 39(2), 530–538 (2009)Google Scholar
  19. 19.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  20. 20.
    Oza, N.C.: Online bagging and boosting. In: 2005 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2340–2345 (2005)Google Scholar
  21. 21.
    Oza, N.C., Russell, S.: Online bagging and boosting. In: Eighth International Workshop on Artificial Intelligence and Statistics, pp. 105–112. Morgan Kaufmann, Key West, Florida, USA (2001) Google Scholar
  22. 22.
    Paisitkriangkrai, S., Shenm, C., Zhang, J.: Fast pedestrian detection using a cascade of boosted covariance features. IEEE Trans. Circuits Syst. Video Technol. 18(8), 1140–1151 (2008)CrossRefGoogle Scholar
  23. 23.
    Parag, T., Porikli, F., Elgammal, A.: Boosting adaptive linear weak classifiers for online learning and tracking. In: International Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  24. 24.
    Parikh, D., Polikar, R.: An ensemble-based incremental learning approach to data fusion. IEEE Trans. Syst. Man Cybern. B 37(2), 437–450 (2007)CrossRefGoogle Scholar
  25. 25.
    Pham, M.-T., Cham, T.-J.: Online learning asymmetric boosted classifiers for object detection. In: International Conference on Computer Vision and Pattern Recognition (CVPR) (2007)Google Scholar
  26. 26.
    Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006)CrossRefGoogle Scholar
  27. 27.
    Porikli, F.: Integral histogram: A fast way to extract histograms in cartesian spaces. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 829–836. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  28. 28.
    Ratsch, G., Mika, S., Scholkopf, B., Muller, K.R.: Constructing boosting algorithms from svms: An application to one-class classification. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1184–1199 (2002)CrossRefGoogle Scholar
  29. 29.
    Ross, D.A., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2007)Google Scholar
  30. 30.
    Snidaro, L., Visentini, I.: Fusion of heterogeneous features via cascaded on-line boosting. In: Proceedings of the Eleventh International Conference on Information Fusion, pp. 1340–1345, Cologne, Germany, 30 June–3 July 3 2008Google Scholar
  31. 31.
    Sochman, J., Matas, J.: Waldboost—learning for time constrained sequential detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, pp. 150–156 (2005)Google Scholar
  32. 32.
    Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  33. 33.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 511–518, Kauai, Hawaii (2001)Google Scholar
  34. 34.
    Visentini, I., Snidaro, L., Foresti, G.L.: On-line boosted cascade for object detection. In: Proceedings of the 19th International Conference on Pattern Recognition (ICPR), Tampa, Florida, USA (2008)Google Scholar
  35. 35.
    Wu, B., Nevatia, R.: Improving part based object detection by unsupervised, online boosting. In: International Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  36. 36.
    Wu, B., Nevatia, R.: Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  37. 37.
    Wu J., Brubaker S.C., Mullin M.D., Rehg J.M.: Fast asymmetric learning for cascade face detection. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 369–382 (2008)CrossRefGoogle Scholar
  38. 38.
    Xiao, R., Zhu, H., Sun, H., Tang, X.: Dynamic cascades for face detection. In: International Conference on Computer Vision, pp. 1–8 (2007)Google Scholar
  39. 39.
    Yamashita, T., Fujiyoshi, H., Lao, S., Kawade, M.: Human tracking based on soft decision feature and online real boosting. In: International Conference on Pattern Recognition (2008)Google Scholar
  40. 40.
    Yao, J., Odobez, J.M.: Fast human detection from videos using covariance features. In: European Conference on Computer Vision Visual Surveillance workshop (ECCV-VS) (2008)Google Scholar
  41. 41.
    Zhang P., Bui T.D., Suen C.Y.: A novel cascade ensemble classifier system with a high recognition performance on handwritten digits. Pattern Recognit. 40(12), 3415–3429 (2007)zbMATHCrossRefGoogle Scholar
  42. 42.
    Zhang, W., Yu, B., Zelinsky, G.J., Samaras, D.: Object class recognition using multiple layer boosting with heterogeneous features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, pp. 323–330. IEEE Computer Society, Washington, DC (2005)Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Ingrid Visentini
    • 1
  • Lauro Snidaro
    • 1
    Email author
  • Gian Luca Foresti
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of UdineUdineItaly

Personalised recommendations