Pedestrian Recognition from a Moving Catadioptric Camera

  • Wolfgang Schulz
  • Markus Enzweiler
  • Tobias Ehlgen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4713)


This paper presents a real-time system for vision-based pedestrian recognition from a moving vehicle-mounted catadioptric camera. For efficiency, a rectification of the catadioptric image using a virtual cylindrical camera is employed. We propose a novel hybrid combination of a boosted cascade of wavelet-based classifiers with a subsequent texture-based neural network involving adaptive local features as final cascade stage. Within this framework, both fast object detection and powerful object classification are combined to increase the robustness of the recognition system. Further, we compare the hybrid cascade framework to a state-of-the-art multi-cue pedestrian recognition system utilizing shape and texture cues. Image distortions of the objects of interest due to the virtual cylindrical camera transformation are both explicitly and implicitly addressed by shape transformations and machine learning techniques. In extensive experiments, both systems under consideration are evaluated on a real-world urban traffic dataset. Results show the contributions of the various components in isolation and document superior performance of the proposed hybrid cascade system.


Pedestrian Detection Slide Window Approach Camera Setup Cascade Stage Shape Template 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Baker, S., Nayar, S.: Single viewpoint catadioptric cameras. In: Benosman, R., Kang, S.B. (eds.) Panoramic Vision, ch. 4, pp. 39–71 (2001)Google Scholar
  2. 2.
    Bertozzi, M., et al.: Stereo vision-based start-inhibit for heavy goods vehicles. In: IEEE Int. Vehicles Symp., pp. 350–355 (2006)Google Scholar
  3. 3.
    Borgefors, G.: Distance transformations in digital images. Computer Vision, Graphics, and Image Processing 34(3), 344–371 (1986)CrossRefGoogle Scholar
  4. 4.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE PAMI 23(6), 681–685 (2001)Google Scholar
  5. 5.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. CVPR (2005)Google Scholar
  6. 6.
    Ehlgen, T., Pajdla, T.: Monitoring surrounding areas of truck-trailer combinations. In: Proc. of the Int. Conf. on Comp. Vis. Sys. (2007)Google Scholar
  7. 7.
    Elzein, H., Lakshmanan, S., Watta, P.: A motion and shape-based pedestrian detection algorithm. In: IEEE Int. Vehicles Symp., pp. 500–504. IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
  8. 8.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proc. of the European Conf. on Comp. Learn. Theory, pp. 23–37 (1995)Google Scholar
  9. 9.
    Gandhi, T., Trivedi, M.M.: Motion-based vehicle surround analysis using an omni-directional camera. In: IEEE Int. Vehicles Symp., pp. 560–565 (2004)Google Scholar
  10. 10.
    Gavrila, D.M.: Sensor-based pedestrian protection. IEEE Int. Sys. 16(6), 77–81 (2001)CrossRefGoogle Scholar
  11. 11.
    Gavrila, D.M., Giebel, J.: Virtual sample generation for template-based shape matching. In: Proc. CVPR, pp. 676–681 (2001)Google Scholar
  12. 12.
    Gavrila, D.M., Munder, S.: Multi-cue pedestrian detection and tracking from a moving vehicle. IJCV 73(1), 41–59 (2007)CrossRefGoogle Scholar
  13. 13.
    Hecht, E.: Optik, 4th edn. (2002)Google Scholar
  14. 14.
    Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: Proc. CVPR, vol. 1, pp. 878–885 (2005)Google Scholar
  15. 15.
    Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE PAMI 23(4), 349–361 (2001)Google Scholar
  16. 16.
    Munder, S., Gavrila, D.M.: An experimental study on pedestrian classification. IEEE PAMI 28(11), 1863–1868 (2006)Google Scholar
  17. 17.
    Papageorgiou, C., Poggio, T.: A trainable system for object detection. IJCV 38, 15–33 (2000)zbMATHCrossRefGoogle Scholar
  18. 18.
    Sochman, J., Matas, J.: Adaboost with totally corrective updates for fast face detection. In: IEEE Int. Conf. on Autom. Face and Gesture Rec., pp. 445–450 (2004)Google Scholar
  19. 19.
    Svoboda, T., Pajdla, T., Hlavac, V.: Central panoramic cameras: Geometry and design. Technical report, Technical University of Prague (December 1997)Google Scholar
  20. 20.
    United Nations Economic Commission for Europe (UNECE). Road traffic accidents (1997),
  21. 21.
    Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. IJCV 63(2), 153–161 (2005)CrossRefGoogle Scholar
  22. 22.
    Wender, S., Löhlein, O., Gross, H.M.: Multiple classifier cascade for vehicle occupant monitoring using an omnidirectional camera. Technical report, Fortschritt-Berichte VDI (2004)Google Scholar
  23. 23.
    Wöhler, C., Anlauf, J.: An adaptable time-delay neural-network algorithm for image sequence analysis. IEEE Transactions on Neural Networks 10(6), 1531–1536 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Wolfgang Schulz
    • 1
  • Markus Enzweiler
    • 2
  • Tobias Ehlgen
    • 1
  1. 1.DaimlerChrysler AG, Group Research & Advanced Engineering 
  2. 2.Univ. of Mannheim, Dept. of Mathematics and Computer Science, CVGPR Group 

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