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Exploring Relevance Vector Machines for Faster Pedestrian Classification

  • Carlos Serra-Toro
  • V. Javier Traver
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7887)

Abstract

While (linear) Support Vector Machines (SVMs) are one of the mainstream choices for pedestrian classification, this work explores the potential benefit of using Relevance Vector Machines (RVMs). Thanks to the sparser representation of RVMs than that of SVMs, it is found that when classifying with a radial-basis function kernel, a ten-fold speed-up is obtained with only a slight degradation of the overall discriminative power. However, the training time of RVMs for this problem turns out to be about two orders of magnitude higher than that of SVMs. But, by simply partitioning the training set into subsets and learning several RVMs, we show that the training time of RVMs can be reduced as much as one order of magnitude, with a minor decay in performance, with respect to the single RVM on the full training set. These findings are encouraging to further study RVMs as a promising learning module beyond the current (linear) SVMs.

Keywords

Pedestrian classification Relevance Vector Machine Support Vector Machine Sparsity Classification time Training time 

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References

  1. 1.
    Gerónimo, D., López, A.M., Sappa, A.D., Graf, T.: Survey of Pedestrian Detection for Advanced Driver Assistance Systems. IEEE Trans. on PAMI 32(7), 1239–1258 (2010)CrossRefGoogle Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR 2005, vol. 1, pp. 886–893 (2005)Google Scholar
  3. 3.
    Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian Detection: An Evaluation of the State of the Art. IEEE Trans. on PAMI 34(4), 743–761 (2012)CrossRefGoogle Scholar
  4. 4.
    Lampert, C.H., Blaschko, M.B., Hofmann, T.: Beyond Sliding Windows: Object Localization by Efficient Subwindow Search. In: CVPR 2008, pp. 1–8 (2008)Google Scholar
  5. 5.
    Pedersoli, M., Gonzàlez, J., Bagdanov, A.D., Villanueva, J.J.: Recursive Coarse-to-Fine Localization for Fast Object Detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 280–293. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Lehmann, A.D., Gehler, P.V., Van Gool, L.: Branch&Rank: Non-Linear Object Detection. In: Hoey, J., McKenna, S., Trucco, E. (eds.) BMVC 2011, pp. 8.1–8.11. BMVA Press (2011)Google Scholar
  7. 7.
    Dollár, P., Belongie, S., Perona, P.: The Fastest Pedestrian Detector in the West. In: Labrosse, F., Zwiggelaar, R., Liu, Y., Tiddeman, B. (eds.) BMVC 2010, pp. 68.1–68.11. BMVA Press (2010)Google Scholar
  8. 8.
    Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: Pedestrian Detection at 100 Frames per Second. In: CVPR 2012, pp. 2903–2910 (2012)Google Scholar
  9. 9.
    Dollár, P., Appel, R., Kienzle, W.: Crosstalk Cascades for Frame-Rate Pedestrian Detection. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 645–659. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Tipping, M.E.: The Relevance Vector Machine. In: Solla, S.A., Leen, T.K., Müller, K.-R. (eds.) Advances in Neural Information Processing Systems 12, pp. 652–658 (2000)Google Scholar
  11. 11.
    Wei, L., Yang, Y., Nishikawa, R.M., Wernick, M.N., Edwards, A.: Relevance Vector Machine for Automatic Detection of Clustered Microcalcifications. IEEE Trans. on Medical Imaging 24(10), 1278–1285 (2005)CrossRefGoogle Scholar
  12. 12.
    Mianji, F.A., Zhang, Y.: Robust Hyperspectral Classification Using Relevance Vector Machine. IEEE Trans. on Geoscience and Remote Sensing 49(6), 2100–2112 (2011)CrossRefGoogle Scholar
  13. 13.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)MATHGoogle Scholar
  14. 14.
    Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, Inc., New York (1998)MATHGoogle Scholar
  15. 15.
    Shawe-Taylor, J., Sun, S.: A Review of Optimization Methodologies in Support Vector Machines. Neurocomputing 74(17), 3609–3618 (2011)CrossRefGoogle Scholar
  16. 16.
    Dollár, P.: Piotr’s Image and Video Matlab Toolbox (PMT), Software available at: http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html
  17. 17.
    King, D.E.: Dlib-ml: A Machine Learning Toolkit. Journal of Machine Learning Research 10, 1755–1758 (2009), Software available at http://dlib.net Google Scholar
  18. 18.
    Sun, S., Shawe-Taylor, J.: Sparse Semi-supervised Learning Using Conjugate Functions. Journal of Machine Learning Research 11, 2423–2455 (2010)MathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Carlos Serra-Toro
    • 1
  • V. Javier Traver
    • 1
  1. 1.Institute of New Imaging Technologies & Departamento de Lenguajes y Sistemas InformáticosUniversitat Jaume ICastellónSpain

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