Fast and Robust Multi-people Tracking from RGB-D Data for a Mobile Robot

  • Filippo Basso
  • Matteo Munaro
  • Stefano Michieletto
  • Enrico Pagello
  • Emanuele Menegatti
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 193)

Abstract

This paper proposes a fast and robust multi-people tracking algorithm for mobile platforms equipped with a RGB-D sensor. Our approach features an efficient point cloud depth-based clustering, an HOG-like classification to robustly initialize a person tracking and a person classifier with online learning to manage the person ID matching even after a full occlusion. For people detection, we make the assumption that people move on a ground plane. Tests are presented on a challenging real-world indoor environment and results have been evaluated with the CLEAR MOT metrics. Our algorithm proved to correctly track 96% of people with very limited ID switches and few false positives, with an average frame rate of 25 fps. Moreover, its applicability to robot-people following tasks have been tested and discussed.

Keywords

People tracking real-time RGB-D data mobile robots 

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References

  1. 1.
    Bajracharya, M., Moghaddam, B., Howard, A., Brennan, S., Matthies, L.H.: A fast stereo-based system for detecting and tracking pedestrians from a moving vehicle. International Journal of Robotics Research 28, 1466–1485 (2009)CrossRefGoogle Scholar
  2. 2.
    Bellotto, N., Hu, H.: Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of bayesian filters. Auton. Robots 28, 425–438 (2010)CrossRefGoogle Scholar
  3. 3.
    Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. J. Image Video Process. 2008, 1:1–1:10 (2008)Google Scholar
  4. 4.
    Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: Robust tracking-by-detection using a detector confidence particle filter. In: IEEE International Conference on Computer Vision (October 2009)Google Scholar
  5. 5.
    Carballo, A., Ohya, A., Yuta, S.: People detection using range and intensity data from multi-layered laser range finders. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5849–5854 (2010)Google Scholar
  6. 6.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (June 2005)Google Scholar
  7. 7.
    Ess, A., Leibe, B., Schindler, K., Van Gool, L.: A mobile vision system for robust multi-person tracking. In: IEEE Conference on Computer Vision and Pattern Recognition 2008, pp. 1–8 (2008)Google Scholar
  8. 8.
    Ess, A., Leibe, B., Schindler, K., Van Gool, L.: Moving obstacle detection in highly dynamic scenes. In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation, ICRA 2009, Piscataway, NJ, USA, pp. 4451–4458 (2009)Google Scholar
  9. 9.
    Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vision 88, 303–338 (2010)CrossRefGoogle Scholar
  10. 10.
    Grabner, H., Bischof, H.: On-line boosting and vision. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1, Washington, DC, USA, pp. 260–267 (2006)Google Scholar
  11. 11.
    Konstantinova, P., Udvarev, A., Semerdjiev, T.: A study of a target tracking algorithm using global nearest neighbor approach. In: Proceedings of the 4th International Conference Conference on Computer Systems and Technologies: e-Learning, New York, NY, USA, pp. 290–295 (2003)Google Scholar
  12. 12.
    Luber, M., Spinello, L., Arras, K.O.: People tracking in rgb-d data with on-line boosted target models. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011 (2011)Google Scholar
  13. 13.
    Martin, C., Schaffernicht, E., Scheidig, A., Gross, H.-M.: Multi-modal sensor fusion using a probabilistic aggregation scheme for people detection and tracking. Robotics and Autonomous Systems 54(9), 721–728 (2006)CrossRefGoogle Scholar
  14. 14.
    Mozos, O., Kurazume, R., Hasegawa, T.: Multi-part people detection using 2d range data. International Journal of Social Robotics 2, 31–40 (2010)CrossRefGoogle Scholar
  15. 15.
    Navarro-Serment, L.E., Mertz, C., Hebert, M.: Pedestrian detection and tracking using three-dimensional ladar data. In: FSR, pp. 103–112 (2009)Google Scholar
  16. 16.
    Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9-13 (2011)Google Scholar
  17. 17.
    Satake, J., Miura, J.: Robust stereo-based person detection and tracking for a person following robot. In: Workshop on People Detection and Tracking IEEE ICRA (2009)Google Scholar
  18. 18.
    Spinello, L., Arras, K.O.: People detection in rgb-d data. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011 (2011)Google Scholar
  19. 19.
    Spinello, L., Arras, K.O., Triebel, R., Siegwart, R.: A layered approach to people detection in 3d range data. In: Proc. 24th AAAI Conference on Artificial Intelligence, PGAI Track (AAAI 2010), Atlanta, USA (2010)Google Scholar
  20. 20.
    Spinello, L., Luber, M., Arras, K.O.: Tracking people in 3d using a bottom-up top-down people detector. In: IEEE International Conference on Robotics and Automation (ICRA 2011), Shanghai (2011)Google Scholar
  21. 21.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR 2001, vol. 1, pp. 511–518 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Filippo Basso
    • 1
  • Matteo Munaro
    • 1
  • Stefano Michieletto
    • 1
  • Enrico Pagello
    • 1
  • Emanuele Menegatti
    • 1
  1. 1.Department of Information EngineeringThe University of PadovaPadovaItaly

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