3D-Guided Multiscale Sliding Window for Pedestrian Detection

  • Alejandro González
  • Gabriel Villalonga
  • German Ros
  • David Vázquez
  • Antonio M. López
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9117)

Abstract

The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multi-modal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy.

References

  1. 1.
    Alonso, I.P., Llorca, D.F., Sotelo, M.A., Bergasa, L.M., de Toro, P.R., Nuevo, J., Ocana, M., Garrido, M.A.: Combination of feature extraction methods for svm pedestrian detection. Trans. Intell. Transport. Sys. 8(2), 292–307 (2007)CrossRefGoogle Scholar
  2. 2.
    Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: Pedestrian detection at 100 frames per second. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA (2012)Google Scholar
  3. 3.
    Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: Proceedings of the British Machine Vision Conference, London, UK (2009)Google Scholar
  4. 4.
    Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  5. 5.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: CVPR 2012 (2012)Google Scholar
  6. 6.
    Gerónimo, D., López, A.: Vision-based Pedestrian Protection Systems for Intelligent Vehicles. Springer Briefs in Computer Science. Springer, New York (2013)Google Scholar
  7. 7.
    Gerónimo, D., Sappa, A., Ponsa, D., López, A.: 2D–3D based on-board pedestrian detection system. J. Comput. Vis. Image Underst. 114(5), 583–595 (2010)CrossRefGoogle Scholar
  8. 8.
    Gu, C., Lim, J.J., Arbelez, P., Malik, J.: Recognition using regions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  9. 9.
    Hosang, J., Benenson, R., Schiele, B.: How good are detection proposals, really? In: Proceedings of the British Machine Vision Conference (2014)Google Scholar
  10. 10.
    Labayrade, R., Aubert, D., Tarel, J.P.: Real time obstacle detection in stereovision on non flat road geometry through “v-disparity" representation. In: IEEE Intelligent Vehicle Symposium (2002)Google Scholar
  11. 11.
    Marin, J., Vázquez, D., López, A., Amores, J., Leibe, B.: Random forests of local experts for pedestrian detection. In: Proceedings of the IEEE International Conference on Computer Vision (2013)Google Scholar
  12. 12.
    Ros, G., Ramos, S., Granados, M., Bakhtiary, A., Vazquez, D., Lopez, A.: Vision-based offline-online paradigm for autonomous driving. In: Winter Conference on Applications of Computer Vision (WACV) (2015)Google Scholar
  13. 13.
    Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. Int. J. Comput. Vision 104(2), 154–171 (2013)CrossRefGoogle Scholar
  14. 14.
    van de Sande, K.E.A., Uijlings, J.R.R., Gevers, T., Smeulders, A.W.M.: Segmentation as selective search for object recognition. In: Proceedings of the IEEE International Conference on Computer Vision (2011)Google Scholar
  15. 15.
    Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan (2009)Google Scholar
  16. 16.
    Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 391–405. Springer, Heidelberg (2014) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alejandro González
    • 1
  • Gabriel Villalonga
    • 1
  • German Ros
    • 1
  • David Vázquez
    • 1
  • Antonio M. López
    • 1
  1. 1.Computer Vision Center and Universitat Autònoma de BarcelonaBellaterra, BarcelonaSpain

Personalised recommendations