Real-Time Vision-Based Pedestrian Detection in a Truck’s Blind Spot Zone Using a Warping Window Approach

  • Kristof Van Beeck
  • Toon Goedemé
  • Tinne Tuytelaars
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 283)

Abstract

In this chapter we present a vision-based pedestrian tracking system targeting a specific application: avoiding accidents in the blind spot zone of trucks. Existing blind spot safety systems do not offer a complete solution to this problem. Therefore we propose an active alarm system, which automatically detects vulnerable road users in blind spot camera images, and warns the truck driver about their presence. The demanding time constraint, the need for a high accuracy and the large distortion that a blind spot camera introduces makes this a challenging task. To achieve this we propose a warping window multi-pedestrian tracking algorithm. Our algorithm achieves real-time performance while maintaining high accuracy. To evaluate our algorithm we recorded several pedestrian datasets with a real blind spot camera mounted on a real truck, consisting of realistic simulated dangerous blind spot situations. Furthermore we recorded and performed preliminary experiments with datasets including bicyclists.

Keywords

Computer vision Pedestrian tracking Real-time  Active safety systems 

References

  1. 1.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 886–893 (2005)Google Scholar
  2. 2.
    Dollár, P., Belongie, S., Perona, P.: The fastest pedestrian detector in the west. In: Proceedings of the British Machine Vision Conference, pp. 68.1–68.11 (2010)Google Scholar
  3. 3.
    Dollár, P., Wojek, C., Schiele, B., Perona, P.: A benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Pedestrian detection (2009)Google Scholar
  4. 4.
    Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: An evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 99 (2011)Google Scholar
  5. 5.
    Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: survey and experiments. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2179–2195 (2009)Google Scholar
  6. 6.
    Ess, A., Leibe, B., Schindler, K., Gool, L.V.: A mobile vision system for robust multi-person tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  7. 7.
    EU: Commision of the european communities, european road safety action programme: mid-term review (22 february 2006)Google Scholar
  8. 8.
    Felzenszwalb, P., Girschick, R., McAllester, D.: Cascade object detection with deformable part models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2010)Google Scholar
  9. 9.
    Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  10. 10.
    Gavrila, D., Munder, S.: Multi-cue pedestrian detection and tracking from a moving vehicle. Int. J. Comput. Vision 73(1), 41–59 (2007)Google Scholar
  11. 11.
    Kalman, R.: A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82, 35–45 (1960)CrossRefGoogle Scholar
  12. 12.
    Lampert, C., Blaschko, M., Hoffmann, T.: Efficient subwindow search: A branch and bound framework for object localization. IEEE Trans. Pattern Anal. Mach. Intell. 31, 2129–2142 (2009)CrossRefGoogle Scholar
  13. 13.
    Martensen, H.: Themarapport vrachtwagenongevallen 2000–2007 (BIVV) (2009)Google Scholar
  14. 14.
    Seitner, F., Hanbury, A.: Fast pedestrian tracking based on spatial features and colour. In: Proceedings of the 11th Computer Vision Winter Workshop (2006)Google Scholar
  15. 15.
    Van Beeck, K., Goedemé, T., Tuytelaars, T.: Towards an automatic blind spot camera: robust real-time pedestrian tracking from a moving camera. In: Proceedings of the twelfth IAPR Conference on Machine Vision Applications, pp. 528–531 (2011)Google Scholar
  16. 16.
    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, pp. 511–518 (2001)Google Scholar
  17. 17.
    Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. Int. J. Comput. Vision 63, 153–161 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kristof Van Beeck
    • 1
    • 2
  • Toon Goedemé
    • 1
    • 2
  • Tinne Tuytelaars
    • 1
    • 2
  1. 1.EAVISEKU Leuven - Campus De NayerSint-Katelijne-WaverBelgium
  2. 2.ESAT/PSI - VISICS, IBBTKU LeuvenLeuvenBelgium

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