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Leg Detection and Tracking for a Mobile Robot and Based on a Laser Device, Supervised Learning and Particle Filtering

  • Eugenio Aguirre
  • Miguel Garcia-Silvente
  • Javier Plata
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 252)

Abstract

People detection and tracking is an essential skill to obtain social and interactive robots. Computer vision has been widely used to solve this task but images are affected by noise and illumination changes. Laser range finder is robust against illumination changes so that it can bring useful information to carry out the detection and tracking. In fact, multisensor approaches are showing the best results. In this work, we present a new method to detect and track people using a laser range finder. Patterns of leg are learnt from 2d laser data using supervised learning. Unlike others leg detection approaches, people can be still or moving at the surroundings of the robot. The method of leg detection is used as observation model in a particle filter to track the motion of a person. Experiments in a real indoor environment have been carried out to validate the proposal.

Keywords

Leg detection and tracking laser range finder supervised learning particle filter mobile robots 

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References

  1. 1.
    Ansuategui, A., Ibarguren, A., Martínez-Otzeta, J.M., Tubío, C., Lazkano, E.: Particle filtering for people following behavior using laser scans and stereo vision. International Journal on Artificial Intelligence Tools 20(2), 313–326 (2011)CrossRefGoogle Scholar
  2. 2.
    Arras, K.O., Grzonka, S., Luber, M., Burgard, W.: Efficient people tracking in laser range data using a multi-hypothesis leg-tracker with adaptive occlusion probabilities. In: Proc. IEEE International Conference on Robotics and Automation (ICRA), pp. 1710–1715 (2008)Google Scholar
  3. 3.
    Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–188 (2002)CrossRefGoogle Scholar
  4. 4.
    Bellotto, N., Hu, H.: Multisensor-based human detection and tracking for mobile service robots. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39(1), 167–181 (2009)CrossRefGoogle Scholar
  5. 5.
    Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: The clear mot metrics. Eurasip Journal on Image and Video Processing (2008)Google Scholar
  6. 6.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), http://www.csie.ntu.edu.tw/~cjlin/libsvm Google Scholar
  7. 7.
    Chang, C.-C., Hsu, C.-W., Lin, C.-J.: A Practical Guide to Support Vector Classification. National Taiwan University (2010)Google Scholar
  8. 8.
    Chung, W., Kim, H., Yoo, Y., Moon, C.-B., Park, J.: The detection and following of human legs through inductive approaches for a mobile robot with a single laser range finder. IEEE Transactions on Industrial Electronics 59(8), 3156–3166 (2012)CrossRefGoogle Scholar
  9. 9.
    Fritsch, J., Kleinehagenbrock, M., Lang, S., Plötz, T., Fink, G.A., Sagerer, G.: Multi-modal anchoring for human-robot interaction. Robotics and Autonomous Systems 43(2-3), 133–147 (2003)CrossRefGoogle Scholar
  10. 10.
    Gavrila, D.M.: The visual analysis of human movement: A survey. Computer Vision and Image Understanding: CVIU 73(1), 82–98 (1999)CrossRefzbMATHGoogle Scholar
  11. 11.
    Hough, V., Paul, C.: Method and means for recognizing complex patterns, U.S. Patent 3069654 (1962)Google Scholar
  12. 12.
    Isard, M., Blake, A.: Condensation-conditional density propagation for visual trackings. International Journal of Computer Vision 29, 5–28 (1998)CrossRefGoogle Scholar
  13. 13.
    Microsoft. Kinect official webpage (2010)Google Scholar
  14. 14.
    Muñoz-Salinas, R., Aguirre, E., García-Silvente, M.: People detection and tracking using stereo vision and color. Image and Vision Computing 25, 995–1007 (2007)CrossRefGoogle Scholar
  15. 15.
    Muñoz-Salinas, R., Aguirre, E., García-Silvente, M., Paúl, R.: A new person tracking method for human-robot interaction intended for mobile devices. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 747–757. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Paúl, R., Aguirre, E., García-Silvente, M., Muñoz-Salinas, R.: A new fuzzy based algorithm for solving stereo vagueness in detecting and tracking people. International Journal of Approximate Reasoning 53, 693–708 (2012)CrossRefGoogle Scholar
  17. 17.
    Schenk, K., Eisenbach, M., Kolarow, A., Gross, H.: Comparison of laser-based person tracking at feet and upper-body height. In: Bach, J., Edelkamp, S. (eds.) KI 2011. LNCS, vol. 7006, pp. 277–288. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Eugenio Aguirre
    • 1
  • Miguel Garcia-Silvente
    • 1
  • Javier Plata
    • 1
  1. 1.Department of Computer Science and A.I., CITIC-UGR E.T.S. Ingenierías en Informática y en TelecomunicacionesUniversity of GranadaGranadaSpain

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