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Range-Based People Detection and Tracking for Socially Enabled Service Robots

Part of the Springer Tracts in Advanced Robotics book series (STAR,volume 76)

Abstract

With a growing number of robots deployed in populated environments, the ability to detect and track humans, recognize their activities, attributes and social relations are key components for future service robots. In this article we will consider fundamentals towards these goals and present several results using 2D range data.We first propose a learning method to detect people in sensory data based on a set of boosted features. The method largely outperforms the state of the art that typically relies on hand-tuned classifiers. Then, we present a person tracking approach based on the detection and fusion of leg tracks. To deal with the frequent occlusion and self-occlusion of legs, we extend a Multi-Hypothesis Tracking (MHT) approach by the ability to explicitly reason about and deal with adaptive occlusion probabilities. Finally, we address the problem of tracking groups of people, a first step towards the recognition of social relations. We further extend the MHT approach by a multiple model hypothesis stage able to reflect split/merge events in group formation processes. The proposed extension is mathematically elegant, runs in real-time and further allows to accurately estimate the number of people in each group. The article concludes with prospects and suggestions for future research.

Keywords

  • False Alarm
  • Mobile Robot
  • Data Association
  • Service Robot
  • AdaBoost Algorithm

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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References

  1. Arras, K.O., Mozos, Ó.M., Burgard, W.: Using boosted features for the detection of people in 2d range data. In: Proc. of the IEEE Int. Conference on Robotics and Automation, ICRA 2007, Rome, Italy (2007)

    Google Scholar 

  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 2008), Pasadena, USA (2008)

    Google Scholar 

  3. Reid, D.B.: An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control AC-24(6), 843–854 (1979)

    CrossRef  Google Scholar 

  4. Hall, E.: Handbook of Proxemics Research. Society for the Anthropology of Visual Communications (1974)

    Google Scholar 

  5. Lau, B., Arras, K.O., Burgard, W.: Multi-model hypothesis group tracking and group size estimation. International Journal of Social Robotics 2(1) (March 2010)

    Google Scholar 

  6. Fod, A., Howard, A., Mataric, M.J.: Laser-based people tracking. In: Proceedings of the IEEE International Conference on Robotics & Automation, ICRA (2002)

    Google Scholar 

  7. Kleinhagenbrock, M., Lang, S., Fritsch, J., Lömker, F., Fink, G.A., Sagerer, G.: Person tracking with a mobile robot based on multi-modal anchoring. In: IEEE International Workshop on Robot and Human Interactive Communication (ROMAN), Berlin, Germany (2002)

    Google Scholar 

  8. Scheutz, M., McRaven, J., Cserey, G.: Fast, reliable, adaptive, bimodal people tracking for indoor environments. In: IEEE/RSJ Int. Conference on Intelligent Robots and Systems, Sendai, Japan (2004)

    Google Scholar 

  9. Schulz, D., Burgard, W., Fox, D., Cremers, A.B.: People tracking with a mobile robot using sample-based joint probabilistic data association filters. International Journal of Robotics Research (IJRR) 22(2), 99–116 (2003)

    CrossRef  Google Scholar 

  10. Topp, E.A., Christensen, H.I.: Tracking for following and passing persons. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Alberta, Canada (2005)

    Google Scholar 

  11. Cui, J., Zha, H., Zhao, H., Shibasaki, R.: Tracking multiple people using laser and vision. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Alberta, Canada (2005)

    Google Scholar 

  12. Xavier, J., Pacheco, M., Castro, D., Ruano, A.: Fast line, arc/circle and leg detection from laser scan data in a player driver. In: Proc. of the IEEE Int. Conference on Robotics & Automation, ICRA 2005 (2005)

    Google Scholar 

  13. Hähnel, R., Burgard, W., Thrun, S.: Map building with mobile robots in dynamic environments. In: Proc. of the IEEE Int. Conference on Robotics and Automation, ICRA (2003)

    Google Scholar 

  14. Viola, P., Jones, M.J.: Robust real-time object detection. In: Proceedings of IEEE Workshop on Statistical and Theories of Computer Vision (2001)

    Google Scholar 

  15. Treptow, A., Zell, A.: Real-time object tracking for soccer-robots without color information. Robotics and Autonomous Systems 48(1), 41–48 (2004)

    CrossRef  Google Scholar 

  16. Mozos, O.M., Stachniss, C., Burgard, W.: Supervised learning of places from range data using AdaBoost. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), Barcelona, Spain, April 2005, pp. 1742–1747 (April 2005)

    Google Scholar 

  17. Rottmann, A., Martínez Mozos, O., Stachniss, C., Burgard, W.: Place classification of indoor environments with mobile robots using boosting. In: Proc. of the National Conference on Artificial Intelligence (AAAI), Pittsburgh, PA, USA, pp. 1306–1311 (2005)

    Google Scholar 

  18. Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3), 297–336 (1999)

    MATH  CrossRef  Google Scholar 

  19. Premebida, C., Nunes, U.: Segmentation and geometric primitives extraction from 2d laser range data for mobile robot applications. In: Robótica 2005 - Scientific Meeting of the 5th National Robotics Festival, Coimbra, Portugal (April 2005)

    Google Scholar 

  20. Aloupis, G.: On computing geometric estimators of location. Ph.D. dissertation, School of Computer Science, McGill University (2001)

    Google Scholar 

  21. Arras, K.O.: Feature-based robot navigation in known and unknown environments. Ph.D. dissertation, Swiss Federal Institute of Technology Lausanne (EPFL), These No. 2765 (2003)

    Google Scholar 

  22. Song, Z., Chen, Y., Ma, L., Chung, Y.C.: Some sensing and perception techniques for an omnidirectional ground vehicle with a laser scanner. In: Proceedings of the 2002 IEEE International Symposium on Intelligent Control (2005)

    Google Scholar 

  23. Kluge, B., Köhler, C., Prassler, E.: Fast and robust tracking of multiple moving objects with a laser range finder. In: Proceedings of the IEEE Int. Conf. on Robotics and Automation (2001)

    Google Scholar 

  24. Zajdel, W., Zivkovic, Z., Kröse, B.J.A.: Keeping track of humans: Have I seen this person before? In: IEEE International Conference on Robotics and Automation, Barcelona, Spain (2005)

    Google Scholar 

  25. Schulz, D.: A probabilistic exemplar approach to combine laser and vision for person tracking. In: Proc. Robotics: Science and Systems, Philadelphia, USA (August 2006)

    Google Scholar 

  26. Mucientes, M., Burgard, W.: Multiple hypothesis tracking of clusters of people. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 692–697 (October 2006)

    Google Scholar 

  27. Taylor, G., Kleeman, L.: A multiple hypothesis walking person tracker with switched dynamic model. In: Proc. of the Australasian Conference on Robotics and Automation, Canberra, Australia (2004)

    Google Scholar 

  28. Cui, J., Zha, H., Zhao, H., Shibasaki, R.: Laser-based interacting people tracking using multi-level observations. In: IEEE/RSJ Int. Conference on Intelligent Robots and Systems, Beijing, China (2006)

    Google Scholar 

  29. Cox, I.J., Hingorani, S.L.: An efficient implementation of Reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(2), 138–150 (1996)

    CrossRef  Google Scholar 

  30. Bar-Shalom, Y., Li, X.-R.: Multitarget-Multisensor Tracking: Principles and Techniques. YBS Publishing, Storrs (1995)

    Google Scholar 

  31. Murty, K.G.: An algorithm for ranking all the assignments in order of increasing cost. Operations Research 16, 682–687 (1968)

    MATH  CrossRef  Google Scholar 

  32. Khan, Z., Balch, T., Dellaert, F.: MCMC data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12) (2006)

    Google Scholar 

  33. McKenna, S.J., Jabri, S., Duric, Z., Rosenfeld, A., Wechsler, H.: Tracking groups of people. Computer Vision and Image Understanding 80(1), 42–56 (2000)

    MATH  CrossRef  Google Scholar 

  34. Gennari, G., Hager, G.D.: Probabilistic data association medhods in visual tracking of groups. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2004)

    Google Scholar 

  35. Bose, B., Wang, X., Grimson, E.: Multi-class object tracking algorithm that handles fragmentation and grouping. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007)

    Google Scholar 

  36. Joo, S.-W., Chellappa, R.: A multiple-hypothesis approach for multiobject visual tracking. IEEE Transactions on Image Processing 16(11), 2849–2854 (2007)

    MathSciNet  CrossRef  Google Scholar 

  37. Lau, B., Arras, K.O., Burgard, W.: Tracking groups of people with a multi-model hypothesis tracker. In: International Conference on Robotics and Automation (ICRA), Kobe, Japan (2009)

    Google Scholar 

  38. Hartigan, J.A.: Clustering Algorithms. John Wiley & Sons (1975)

    Google Scholar 

  39. Dubuisson, M.P., Jain, A.K.: A modified Hausdorff distance for object matching. In: Intl. Conference on Pattern Recognition, Jerusalem, Israel, vol. 1, pp. A:566–A:568 (1994)

    Google Scholar 

  40. Cox, I.J., Miller, M.L.: On finding ranked assignments with application to multi-target tracking and motion correspondence. IEEE Trans. on Aerospace and Electronic Systems 31(1), 486–489 (1995)

    CrossRef  Google Scholar 

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Correspondence to Kai O. Arras .

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Arras, K.O. et al. (2012). Range-Based People Detection and Tracking for Socially Enabled Service Robots. In: , et al. Towards Service Robots for Everyday Environments. Springer Tracts in Advanced Robotics, vol 76. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25116-0_18

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  • DOI: https://doi.org/10.1007/978-3-642-25116-0_18

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