Advertisement

A New Pedestrian Detection Descriptor Based on the Use of Spatial Recurrences

  • Carlos Serra-Toro
  • V. Javier Traver
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6855)

Abstract

Recent work on pedestrian detection has relied on the concept of local co-occurences of features to propose higher-order, richer descriptors. While this idea has proven to be benefitial for this detection task, it fails to properly account for a more general and/or holistic representation. In this paper, a novel, flexible, and modular descriptor is proposed which is based on the alternative concept of visual recurrence and, in particular, on a mathematically sound tool, the recurrence plot. The experimental work conducted provides evidence on the discriminatory power of the descriptor, with results comparable to recent similar approaches. Furthermore, since its degree of locality, its visual compactness, and the pair-wise feature similarity can be easily changed, it holds promise to account for characterizations of other descriptors, as well as for a range of accuracy-computational trade-offs for pedestrian detection and, possibly, also for other object detection problems.

Keywords

Pedestrian detection Recurrence plot Oriented gradients Feature descriptor 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Munder, S., Gavrila, D.M.: An Experimental Study on Pedestrian Classification. IEEE Trans. on PAMI 28(11), 1863–1868 (2006)CrossRefGoogle Scholar
  2. 2.
    Marwan, N., Romano, M.C., Thiel, M., Kurths, J.: Recurrence Plots for the Analysis of Complex Systems. Physics Reports 438(5–6), 237–329 (2007)CrossRefGoogle Scholar
  3. 3.
    Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)CrossRefGoogle Scholar
  4. 4.
    Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research 9, 1871–1874 (2008), Software available at http://www.csie.ntu.edu.tw/~cjlin/liblinear zbMATHGoogle Scholar
  5. 5.
    Enzweiler, M., Gavrila, D.M.: Monocular Pedestrian Detection: Survey and Experiments. IEEE Trans. on PAMI 31(12), 2179–2195 (2009)CrossRefGoogle Scholar
  6. 6.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: IEEE Conf. on CVPR, vol. 1, pp. 886–893 (2005)Google Scholar
  7. 7.
    Watanabe, T., Ito, S., Yokoi, K.: Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection. In: Wada, T., Huang, F., Lin, S. (eds.) PSIVT 2009. LNCS, vol. 5414, pp. 37–47. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Ito, S., Kubota, S.: Object Classification Using Heterogeneous Co-occurrence Features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 209–222. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Fawcett, T.: An Introduction to ROC Analysis. Pattern Recognition Letters 27(8), 861–874 (2006)CrossRefGoogle Scholar
  10. 10.
    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. LNCS, vol. 6316, pp. 280–293. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object Detection with Discriminatively Trained Part-Based Models. IEEE Trans. on PAMI 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  12. 12.
    Zhu, Q., Avidan, S., Yeh, M.-C., Cheng, K.-T.: Fast Human Detection Using a Cascade of Histograms of Oriented Gradients. In: IEEE Conf. on CVPR, vol. 2, pp. 1491–1498 (2006)Google Scholar
  13. 13.
    Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian Detection: A Benchmark. In: IEEE Conf. on CVPR, pp. 304–311 (2009)Google Scholar
  14. 14.
    Lampert, C.H., Blaschko, M.B., Hofmann, T.: Beyond Sliding Windows: Object Localization by Efficient Subwindow Search. In: IEEE Conf. on CVPR, pp. 1–8 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

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

Personalised recommendations