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Predicting Pedestrian Trajectories Using Velocity-Space Reasoning

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Algorithmic Foundations of Robotics X

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 86))

Abstract

We introduce a novel method to predict pedestrian trajectories using agent-based velocity-space reasoning for improved human-robot interaction. This formulation models the trajectory of each moving pedestrian in a robot’s environment using velocity obstacles and learns the simulation parameters based on tracked data. The resulting motion model for each agent is computed using statistical inferencing techniques from noisy data. This includes the combination of Ensemble Kalman filters and maximum likelihood estimation algorithm to learn individual motion parameters at interactive rates. We highlight the performance of our motion model in real-world crowded scenarios. We also compare its performance with prior techniques and demonstrate improved accuracy in the predicted trajectories.

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References

  1. Bennewitz, M., Burgard, W., Cielniak, G., Thrun, S.: Learning motion patterns of people for compliant robot motion. The International Journal of Robotics Research 24(1), 31–48 (2005)

    Article  Google Scholar 

  2. Van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: IEEE International Conference on Robotics and Automation, ICRA 2008, pp. 1928–1935 (2008)

    Google Scholar 

  3. van den Berg, J., Patil, S., Sewall, J., Manocha, D., Lin, M.: Interactive navigation of individual agents in crowded environments. In: Symp. on Interactive 3D Graphics and Games (I3D 2008) (2008)

    Google Scholar 

  4. Boltes, M., Seyfried, A., Steffen, B., Schadschneider, A.: Automatic extraction of pedestrian trajectories from video recordings. Pedestrian and Evacuation Dynamics 2008, 43–54 (2010)

    Google Scholar 

  5. Bruce, A., Gordon, G.: Better motion prediction for people-tracking (2004)

    Google Scholar 

  6. Casella, G., Berger, R.: Statistical inference. Duxbury advanced series in statistics and decision sciences. Thomson Learning (2002)

    Google Scholar 

  7. Cui, J., Zha, H., Zhao, H., Shibasaki, R.: Tracking multiple people using laser and vision (2005)

    Google Scholar 

  8. Fod, A., Howard, A., Mataric, M.: A laser-based people tracker (2002)

    Google Scholar 

  9. Gong, H., Sim, J., Likhachev, M., Shi, J.: Multi-hypothesis motion planning for visual object tracking

    Google Scholar 

  10. Guy, S., Kim, S., Lin, M., Manocha, D.: Simulating heterogeneous crowd behaviors using personality trait theory. In: Symp. on Computer Animation, pp. 43–52 (2011)

    Google Scholar 

  11. Guy, S.J., Chhugani, J., Curtis, S., Dubey, P., Lin, M., Manocha, D.: PLEdestrians: a least-effort approach to crowd simulation. In: Symp. on Computer Animation, pp. 119–128 (2010)

    Google Scholar 

  12. Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Physical Review E 51(5), 4282 (1995)

    Article  Google Scholar 

  13. Karamouzas, I., Heil, P., van Beek, P., Overmars, M.: A predictive collision avoidance model for pedestrian simulation. Motion in Games, 41–52 (2009)

    Google Scholar 

  14. Karamouzas, I., Overmars, M.: A velocity-based approach for simulating human collision avoidance (2010)

    Google Scholar 

  15. Kratz, L., Nishino, K.: Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence (99), 1 (2011)

    Google Scholar 

  16. Lerner, A., Fitusi, E., Chrysanthou, Y., Cohen-Or, D.: Fitting behaviors to pedestrian simulations. In: Symp. on Computer Animation, pp. 199–208 (2009)

    Google Scholar 

  17. Liao, L., Fox, D., Hightower, J., Kautz, H., Schulz, D.: Voronoi tracking: Location estimation using sparse and noisy sensor data (2003)

    Google Scholar 

  18. Luber, M., Stork, J., Tipaldi, G., Arras, K.: People tracking with human motion predictions from social forces. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 464–469 (2010)

    Google Scholar 

  19. McLachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions, 2nd edn. Wiley Series in Probability and Statistics. Wiley-Interscience

    Google Scholar 

  20. Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 935–942 (2009)

    Google Scholar 

  21. Pelechano, N., Allbeck, J., Badler, N.: Virtual crowds: Methods, simulation, and control. Synthesis Lectures on Computer Graphics and Animation 3(1), 1–176 (2008)

    Article  Google Scholar 

  22. Pellegrini, S., Ess, A., Schindler, K., Van Gool, L.: You’ll never walk alone: Modeling social behavior for multi-target tracking. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 261–268 (2009)

    Google Scholar 

  23. Pettré, J., Ondřej, J., Olivier, A.H., Cretual, A., Donikian, S.: Experiment-based modeling, simulation and validation of interactions between virtual walkers. In: Symp. on Computer Animation, pp. 189–198 (2009)

    Google Scholar 

  24. Reynolds, C.W.: Steering behaviors for autonomous characters. In: Game Developers Conference (1999), http://www.red3d.com/cwr/steer/gdc99

  25. Rodriguez, M., Ali, S., Kanade, T.: Tracking in unstructured crowded scenes. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1389–1396 (2009)

    Google Scholar 

  26. Schadschneider, A., Klingsch, W., Klüpfel, H., Kretz, T., Rogsch, C., Seyfried, A.: Evacuation dynamics: Empirical results, modeling and applications. Arxiv preprint arXiv:0802.1620 (2008)

    Google Scholar 

  27. Schulz, D., Burgard, W., Fox, D., Cremers, A.: People tracking with mobile robots using sample-based joint probabilistic data association filters. The International Journal of Robotics Research 22(2), 99 (2003)

    Article  Google Scholar 

  28. Treuille, A., Cooper, S., Popović, Z.: Continuum crowds. ACM SIGGRAPH 2006 Papers, 1160–1168 (2006)

    Google Scholar 

  29. Van Den Berg, J., Guy, S., Lin, M., Manocha, D.: Reciprocal n-body collision avoidance. Robotics Research, 3–19 (2011)

    Google Scholar 

  30. Ziebart, B.D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Bagnell, J.A., Hebert, M., Dey, A.K., Srinivasa, S.: Planning-based prediction for pedestrians. In: Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 3931–3936. IEEE Press (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of North Carolina, Chapel Hill, USA

    Sujeong Kim, Stephen J. Guy, Ming C. Lin & Dinesh Manocha

  2. City University of Hong Kong, Hong Kong, China

    Wenxi Liu & Rynson W. H. Lau

Authors
  1. Sujeong Kim
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  2. Stephen J. Guy
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  3. Wenxi Liu
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  4. Rynson W. H. Lau
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  5. Ming C. Lin
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  6. Dinesh Manocha
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Corresponding author

Correspondence to Sujeong Kim .

Editor information

Editors and Affiliations

  1. Associate Professor of Aeronautics and, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Emilio Frazzoli

  2. MIT Comp. Sci. & Artificial Intel. Lab., Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Tomas Lozano-Perez

  3. Robust Robotics Group, Massachusetts Institute of Technology CSAIL, Cambridge, Massachusetts, USA

    Nicholas Roy

  4. Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Daniela Rus

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© 2013 Springer-Verlag Berlin Heidelberg

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Kim, S., Guy, S.J., Liu, W., Lau, R.W.H., Lin, M.C., Manocha, D. (2013). Predicting Pedestrian Trajectories Using Velocity-Space Reasoning. In: Frazzoli, E., Lozano-Perez, T., Roy, N., Rus, D. (eds) Algorithmic Foundations of Robotics X. Springer Tracts in Advanced Robotics, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36279-8_37

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  • DOI: https://doi.org/10.1007/978-3-642-36279-8_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36278-1

  • Online ISBN: 978-3-642-36279-8

  • eBook Packages: EngineeringEngineering (R0)

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