Skip to main content
SpringerLink
Log in

Cascaded online boosting

  • Special Issue
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Amit, Y., Geman, D.: A computational model for visual selection. Neural Comput. 11(7), 1691–1715 (1999)

    Article  Google Scholar 

  2. Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal Mach. Intell. 29(2), 261–271 (2007)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  6. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Thirteen International Conference on Machine Learning, pp. 148–156 (1996)

  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. 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)

    Article  Google Scholar 

  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)

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  15. Li, S.Z., Zhang, Z.: Floatboost learning and statistical face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26(9):1112–1123 (2004)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  17. Liu, X.M., Yu, T.: Gradient feature selection for online boosting. In: International Conference on Computer Vision, pp. 1–8 (2007)

  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. 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)

    Article  Google Scholar 

  20. Oza, N.C.: Online bagging and boosting. In: 2005 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2340–2345 (2005)

  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)

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

  26. Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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. 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 2008

  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)

  32. Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  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)

  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)

  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)

  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)

  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)

    Article  Google Scholar 

  38. Xiao, R., Zhu, H., Sun, H., Tang, X.: Dynamic cascades for face detection. In: International Conference on Computer Vision, pp. 1–8 (2007)

  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)

  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)

  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)

    Article  MATH  Google Scholar 

  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)

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Udine, Udine, Italy

    Ingrid Visentini, Lauro Snidaro & Gian Luca Foresti

Authors
  1. Ingrid Visentini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lauro Snidaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gian Luca Foresti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lauro Snidaro.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Visentini, I., Snidaro, L. & Foresti, G.L. Cascaded online boosting. J Real-Time Image Proc 5, 245–257 (2010). https://doi.org/10.1007/s11554-010-0154-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-010-0154-9

Keywords

Search

Navigation