Counting Pedestrians in Bidirectional Scenarios Using Zenithal Depth Images

  • Pablo Vera
  • Daniel Zenteno
  • Joaquín Salas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7914)

Abstract

In this document, we describe a people counting system that can precisely detect people as they are seen from a zenithal depth camera pointing at the floor. In particular, we are interested in scenarios where there are two preferred directions of motion. In our framework, we detect people using a Support Vector Machine classifier, follow their trajectory by modeling the problem of matching observations between frames as a bipartite graph, and determine the direction of their motion with a bi-directional classifier. We include experimental evidence, from four different scenarios, for each major stage of our method.

Keywords

Support Vector Machine Bipartite Graph Depth Image Driver Assistance System Oriented Gradient 
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.

References

  1. 1.
    Chan, A., Liang, Z., Vasconcelos, N.: Privacy Preserving Crowd Monitoring: Counting People without People Models or Tracking. In: CVPR, pp. 1–7 (2008)Google Scholar
  2. 2.
    Chen, T., Chen, T., Chen, Z.: An Intelligent People-flow Counting Method for Passing through a Gate. In: Conference on Robotics, Automation and Mechatronics, pp. 1–6 (2006)Google Scholar
  3. 3.
    Chowdhury, A., Chatterjee, R., Ghosh, M., Ray, N.: Cell Tracking in Video Microscopy using Bipartite Graph Matching. In: ICPR, pp. 2456–2459. IEEE (2010)Google Scholar
  4. 4.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines: And other Kernel-based Learning Methods. Cambridge University Press (2000)Google Scholar
  5. 5.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR, pp. 886–893 (2005)Google Scholar
  6. 6.
    Enzweiler, M., Gavrila, D.: Monocular Pedestrian Detection: Survey and Experiments. IEEE Trans. on Pattern Anal. and Mach. Intell. 31(12), 2179–2195 (2009)CrossRefGoogle Scholar
  7. 7.
    Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes (VOC) Challenge. Int. J. Comput. Vision 88(2), 303–338 (2010)CrossRefGoogle Scholar
  8. 8.
    Fischler, M., Bolles, R.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Geronimo, D., Lopez, A., Sappa, A., Graf, T.: Survey of Pedestrian Detection for Advanced Driver Assistance Systems. IEEE Trans. on Pattern Anal. and Mach. Intell. 32(7), 1239–1258 (2010)CrossRefGoogle Scholar
  10. 10.
    Haubner, N., Schwanecke, U., Dorner, R., Lehmann, S., Luderschmidt, J.: Towards a Top-View Detection of Body Parts in an Interactive Tabletop Environment. In: Architectures of Computing Systems (2012)Google Scholar
  11. 11.
    Kilambi, P., Ribnick, E., Joshi, A., Masoud, O., Papanikolopoulos, N.: Estimating Pedestrian Counts in Groups. Computer Vision and Image Understanding 110(1), 43–59 (2008)CrossRefGoogle Scholar
  12. 12.
    Kong, D., Gray, D., Tao, H.: A Viewpoint Invariant Approach for Crowd Counting. In: ICPR, vol. 3, pp. 1187–1190 (2006)Google Scholar
  13. 13.
    Kuhn, H.: The Hungarian Method for the Assignment Problem. Naval Research Logistics Quarterly 2(1-2), 83–97 (2006)CrossRefGoogle Scholar
  14. 14.
    Li, J., Huang, L., Liu, C.: Robust People Counting in Video Surveillance: Dataset and System. In: AVSS, pp. 54–59 (2011)Google Scholar
  15. 15.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes. Cambridge University Press (2007)Google Scholar
  16. 16.
    Raheja, J., Dutta, P., Kalita, S., Lovendra, S.: An Insight into the Algorithms on Real-Time People Tracking and Counting System. Int. J. of Comp. Appl. 46(5), 1–6 (2012)Google Scholar
  17. 17.
    Rowan, M., Maire, F.D.: An Efficient Multiple Object Vision Tracking System using Bipartite Graph Matching. In: FIRA Robot World Congress. FIRA Robot World Congress (2004)Google Scholar
  18. 18.
    Salti, S., Cavallaro, A., Di Stefano, L.: Adaptive Appearance Modeling for Video Tracking: Survey and Evaluation. IEEE Trans. on Image Process. (2012)Google Scholar
  19. 19.
    Seber, G.: Multivariate observations. Wiley (1984)Google Scholar
  20. 20.
    Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: CVPR (2011)Google Scholar
  21. 21.
    Spinello, L., Arras, K.: People Detection in RGB-D Data. In: IROS, pp. 3838–3843 (2011)Google Scholar
  22. 22.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and Practice of Background Maintenance. In: ICCV, p. 255 (1999)Google Scholar
  23. 23.
    Yahiaoui, T., Meurie, C., Khoudour, L., Cabestaing, F.: A People Counting System based on Dense and Close Stereovision. In: Image and Signal Processing, pp. 59–66 (2008)Google Scholar
  24. 24.
    Zhang, E., Chen, F.: A Fast and Robust People Counting Method in Video Surveillance. In: International Conference on Computational Intelligence and Security, pp. 339–343 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pablo Vera
    • 1
  • Daniel Zenteno
    • 1
  • Joaquín Salas
    • 1
  1. 1.Instituto Politécnico NacionalColinas del CimatarioMéxico

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