Machine Vision and Applications

, Volume 26, Issue 5, pp 561–573 | Cite as

RARE: people detection in crowded passages by range image reconstruction

  • Tim van Oosterhout
  • Gwenn Englebienne
  • Ben Kröse
Original Paper


In this paper, we address the problem of people detection and tracking in crowded scenes using range cameras. We propose a new method for people detection and localisation based on the combination of background modelling and template matching. The method uses an adaptive background model in the range domain to characterise the scene without people. Then a 3D template is placed in possible people locations by projecting it in the background to reconstruct a range image that is most similar to the observed range image. We tested the method on a challenging outdoor dataset and compared it to two methods that each shares one characteristic with the proposed method: a similar template-based method that works in 2D and a well-known baseline method that works in the range domain. Our method performs significantly better, does not deteriorate in crowded environments and runs in real time.


People detection Range reconstruction Stereo images Template 



The research reported in this article was supported by SIA—Stichting Innovatie Alliantie with funding from the Dutch Ministry of Education, Culture and Science (OCW), in the framework of the Balance-IT project. This publication was supported by the Dutch national programme COMMIT in the ‘Virtual worlds for well-being’ project.


  1. 1.
    Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. Comput. Vis. ECCV 5303, 1–14 (2008)MathSciNetGoogle Scholar
  2. 2.
    Kong, D., Gray, D., Tao, H.: A viewpoint invariant approach for crowd counting. In: Pattern Recognition, 2006. 18th International Conference on ICPR 2006. vol. 3, pp. 1187–1190 (2006)Google Scholar
  3. 3.
    Zhao, T., Nevatia, R., Wu, B.: Segmentation and tracking of multiple humans in crowded environments. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1198–1211 (2008)CrossRefGoogle Scholar
  4. 4.
    Harville, M.: Stereo person tracking with short and long term planview appearance models of shape and color. In: Proceedings IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 522–527 (2005)Google Scholar
  5. 5.
    Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multicamera people tracking with a probabilistic occupancy map. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 267–282 (2008)CrossRefGoogle Scholar
  6. 6.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, vol. 2 (1999)Google Scholar
  7. 7.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Realtime foreground-background segmentation using codebook model. Real-time imag. 11(3), 172–185 (2005)CrossRefGoogle Scholar
  8. 8.
    Cristani, M., Farenzena, M., Bloisi, D., Murino, V.: Background subtraction for automated multisensor surveillance: a comprehensive review. EURASIP J. Adv. Signal Process. 2010, 43 (2010)CrossRefGoogle Scholar
  9. 9.
    Harville, M.: Stereo person tracking with adaptive plan-view templates of height and occupancy statistics. Image Vis. Comput. 22(2), 127–142 (2004)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Muñoz-Salinas, R., Aguirre, E., García-Silvente, M.: People detection and tracking using stereo vision and color. Image Vis. Comput. 25(6), 995–1007 (2007)CrossRefGoogle Scholar
  11. 11.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001, vol. 1, p. I511 (2001)Google Scholar
  12. 12.
    Gavrila, D.: Pedestrian detection from a moving vehicle. Comput. Vis. ECCV 2000, 37–49 (2000)Google Scholar
  13. 13.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005. vol. 1, pp. 886–893 (2005)Google Scholar
  14. 14.
    Li, M., Zhang, Z., Huang, K., Tan, T.: Rapid and robust human detection and tracking based on omega-shape features. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 2545–2548 (2009)Google Scholar
  15. 15.
    Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1385–1392 (2011)Google Scholar
  16. 16.
    Camplani, M., del Blanco, C.R., Salgado, L., Jaureguizar, F., Garca, N.: Advanced background modeling with RGB-d sensors through classifiers combination and inter-frame foreground prediction. Mach. Vis. Appl. p. 114 (2013)Google Scholar
  17. 17.
    Wu, S., Moore, B.E., Shah, M.: Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2054–2060 (2010). doi: 10.1109/CVPR.2010.5539882
  18. 18.
    Moore, B.E., Ali, S., Mehran, R., Shah, M.: Visual crowd surveillance through a hydrodynamics lens. Commun. ACM 54(12), 64–73 (2011). doi: 10.1145/2043174.2043192 CrossRefGoogle Scholar
  19. 19.
    Englebienne, G., Kröse, B.J.A.: Fast bayesian people detection. In: Proceedings of the 22nd Benelux AI Conference, BNAIC (2010)Google Scholar
  20. 20.
    Point Grey Research Inc.: Triclops stereo vision SDK reference version 3.3.1 (2010)Google Scholar
  21. 21.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002)zbMATHCrossRefGoogle Scholar
  22. 22.
    Stone, E., Skubic, M.: Evaluation of an inexpensive depth camera for in-home gait assessment. J. Ambient Intell. Smart Environ. 3(4), 349–361 (2011)Google Scholar
  23. 23.
    Greff, K., Brandão, A., Krauß, S., Stricker, D., Clua, E.: A comparison between background subtraction algorithms using a consumer depth camera. In: Proceedings of the International Conference on Computer Vision Theory and Applications, vol. 1 (SciTePress, Rome, 2012), vol. 1, pp. 431–436 (2012)Google Scholar
  24. 24.
    Bouwmans, T.: Recent advanced statistical background modeling for foreground detection-a systematic survey. Recent Pat. Comput. Sci. 4(3), 147–176 (2011)Google Scholar
  25. 25.
    Fernandez-Sanchez, E.J., Diaz, J., Ros, E.: Background subtraction based on color and depth using active sensors. Sensors 13(7), 8895–8915 (2013)CrossRefGoogle Scholar
  26. 26.
    Williams, C., Titsias, M.: Greedy learning of multiple objects in images using robust statistics and factorial learning. Neural Comput. 16(5), 1039–1062 (2004)zbMATHCrossRefGoogle Scholar
  27. 27.
    Beymer, D.: Person counting using stereo. In: Workshop on Human Motion, pp. 127–133 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Tim van Oosterhout
    • 1
  • Gwenn Englebienne
    • 2
  • Ben Kröse
    • 1
    • 2
  1. 1.University of Applied Sciences Amsterdam (HvA)AmsterdamThe Netherlands
  2. 2.University of Amsterdam (UvA)AmsterdamThe Netherlands

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