Following a Large Unpredictable Group of Targets among Obstacles

  • Christopher Vo
  • Jyh-Ming Lien
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6459)


Camera control is essential in both virtual and real-world environments. Our work focuses on an instance of camera control called target following, and offers an algorithm, based on the ideas of monotonic tracking regions and ghost targets, for following a large coherent group of targets with unknown trajectories, among known obstacles. In multiple-target following, the camera’s primary objective is to follow and maximize visibility of multiple moving targets. For example, in video games, a third-person view camera may be controlled to follow a group of characters through complicated virtual environments. In robotics, a camera attached to robotic manipulators could also be controlled to observe live performers in a concert, monitor assembly of a mechanical system, or maintain task visibility during teleoperated surgical procedures. To the best of our knowledge, this work is the first attempting to address this particular instance of camera control.


Motion planning camera planning target following group motion monitoring monotonic tracking regions ghost targets 


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  1. 1.
    Bandyopadhyay, T., Li, Y., Ang Jr., M., Hsu, D.: A greedy strategy for tracking a locally predictable target among obstacles. In: Proc. IEEE Int. Conf. on Robotics & Automation, pp. 2342–2347 (2006)Google Scholar
  2. 2.
    Bares, W.H., Grégoire, J.P., Lester, J.C.: Realtime constraint-based cinematography for complex interactive 3d worlds. In: AAAI 1998/IAAI 1998, pp. 1101–1106. AAAI, Menlo Park (1998)Google Scholar
  3. 3.
    Becker, C., González-Banos, H., Latombe, J.C., Tomasi, C.: An intelligent observer. In: The 4th International Symposium on Experimental Robotics IV, pp. 153–160. Springer, London (1997)Google Scholar
  4. 4.
    Bhattacharya, S., Hutchinson, S.: On the existence of nash equilibrium for a two-player pursuit-evasion game with visibility constraints. Int. J. of Rob. Res. 57, 251–265 (2009)zbMATHGoogle Scholar
  5. 5.
    Butz, A.: Anymation with cathi. In: Proceedings of the 14th Annual National Conference on Artificial Intelligence (AAAI/IAAI), pp. 957–962 (1997)Google Scholar
  6. 6.
    Courty, N., Marchand, E.: Computer animation: a new application for image-based visual servoing. In: Proceedings 2001 ICRA, IEEE International Conference on Robotics and Automation, vol. 1, pp. 223–228 (2001)Google Scholar
  7. 7.
    Drucker, S.M., Zeltzer, D.: Intelligent camera control in a virtual environment. In: Proceedings of Graphics Interface 1994, pp. 190–199 (1994)Google Scholar
  8. 8.
    Geraerts, R.: Camera planning in virtual environments using the corridor map method. In: Egges, A., Geraerts, R., Overmars, M. (eds.) MIG 2009. LNCS, vol. 5884, pp. 194–209. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Gleicher, M., Witkin, A.: Through-the-lens camera control. In: SIGGRAPH 1992: Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques, pp. 331–340. ACM, New York (1992)CrossRefGoogle Scholar
  10. 10.
    Goemans, O., Overmars, M.: Automatic generation of camera motion to track a moving guide. In: International Workshop on the Algorithmic Foundations of Robotics, pp. 187–202 (2004)Google Scholar
  11. 11.
    Gonzalez-Banos, H., Lee, C.Y., Latombe, J.C.: Real-time combinatorial tracking of a target moving unpredictably among obstacles. In: Proceedings IEEE International Conference on Robotics and Automation, vol. 2, pp. 1683–1690 (2002)Google Scholar
  12. 12.
    Halper, N., Helbing, R., Strothotte, T.: A camera engine for computer games: Managing the trade-off between constraint satisfaction and frame coherence. Computer Graphics Forum 20(3), 174–183 (2002)CrossRefGoogle Scholar
  13. 13.
    Hughes, S., Lewis, M.: Robotic camera control for remote exploration. In: CHI 2004: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 511–517. ACM, New York (2004)CrossRefGoogle Scholar
  14. 14.
    Jung, B., Sukhatme, G.: A region-based approach for cooperative multi-target tracking in a structured environment. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 2764–2769 (2002)Google Scholar
  15. 15.
    LaValle, S., Gonzalez-Banos, H., Becker, C., Latombe, J.C.: Motion strategies for maintaining visibility of a moving target. In: Proceedings IEEE International Conference on Robotics and Automation, vol. 1, pp. 731–736 (1997)Google Scholar
  16. 16.
    Li, T.Y., Cheng, C.C.: Real-time camera planning for navigation in virtual environments. In: Butz, A., Fisher, B., Krüger, A., Olivier, P., Christie, M. (eds.) SG 2008. LNCS, vol. 5166, pp. 118–129. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Murrieta-Cid, R., Tovar, B., Hutchinson, S.: A sampling-based motion planning approach to maintain visibility of unpredictable targets. Auton. Robots 19(3), 285–300 (2005)CrossRefGoogle Scholar
  18. 18.
    Oskam, T., Sumner, R.W., Thuerey, N., Gross, M.: Visibility transition planning for dynamic camera control. In: SCA 2009: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 55–65 (2009)Google Scholar
  19. 19.
    Parker, L.E.: Distributed algorithms for multi-robot observation of multiple moving targets. Auton. Robots 12(3), 231–255 (2002)CrossRefzbMATHGoogle Scholar
  20. 20.
    Schulz, D., Burgard, W., Fox, D., Cremers, A.B.: People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters. The International Journal of Robotics Research 22(2), 99–116 (2003)CrossRefGoogle Scholar
  21. 21.
    Seligmann, D.D., Feiner, S.: Automated generation of intent-based 3d illustrations. SIGGRAPH Comput. Graph. 25(4), 123–132 (1991)CrossRefGoogle Scholar
  22. 22.
    Vo, C., Lien, J.M.: Visibility-based strategies for searching and tracking unpredictable coherent targets among known obstacles. In: ICRA 2010 Workshop: Search and Pursuit/Evasion in the Physical World (2010)Google Scholar
  23. 23.
    Wilmarth, S.A., Amato, N.M., Stiller, P.F.: MAPRM: A probabilistic roadmap planner with sampling on the medial axis of the free space. In: Proc. of IEEE Int. Conf. on Robotics and Automation, vol. 2, pp. 1024–1031 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Christopher Vo
    • 1
  • Jyh-Ming Lien
    • 1
  1. 1.Department of Computer ScienceGeorge Mason UniversityFairfaxUSA

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