Behavioral Analysis of Mobile Robot Trajectories Using a Point Distribution Model

  • Pierre Roduit
  • Alcherio Martinoli
  • Jacques Jacot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)


In recent years, the advent of robust tracking systems has enabled behavioral analysis of individuals based on their trajectories. An analysis method based on a Point Distribution Model (PDM) is presented here. It is an unsupervised modeling of the trajectories in order to extract behavioral features. The applicability of this method has been demonstrated on trajectories of a realistically simulated mobile robot endowed with various controllers that lead to different patterns of motion. Results show that this analysis method is able to clearly classify controllers in the PDM-transformed space, an operation extremely difficult in the original space. The analysis also provides a link between the behaviors and trajectory differences.


Mobile Robot Deformation Mode Mahalanobis Distance Video Surveillance Behavioral Analysis 
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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  1. 1.
    Bertini, M., Del Bimbo, A., Nunziati, W.: Highlights modeling and detection in sports videos. Pattern Analysis and Application 7(4), 411–421 (2005)CrossRefGoogle Scholar
  2. 2.
    Cootes, T., Taylor, C., Cooper, D.: Active shape-models - their training and applications. Vision and Image Understanding, 38–59 (1995)Google Scholar
  3. 3.
    de Meneses, Y.L., Roduit, P., Luisier, F., Jacot, J.: Trajectory analysis for sport and video surveillance. Electronic Letters on Computer Vision and Image Analysis 5(3), 148–156 (2005)Google Scholar
  4. 4.
    Egerstedt, M., Balch, T., Dellaert, F., Delmotte, F., Khan, Z.: What are the ants doing? vision-based tracking and reconstruction of control programs. In: IEEE International Conference on Robotics and Automation, pp. 4193–4198. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  5. 5.
    Grimson, W.E.L., Stauffer, C., Romano, R., Lee, L.: Using adaptive tracking to classify and monitor activities in a site. In: Conference on Computer Vision and Pattern Recognition, pp. 22–29. IEEE Computer Society, Los Alamitos (1998)Google Scholar
  6. 6.
    Hayes, A.T., Martinoli, A., Goodman, R.M.: Distributed odor source localization. In: Gardner, J.W., Nagle, H.T., Persaud (eds.) Special Issue in Artificial Olfaction, IEEE Sensors Journal, vol. 2, pp. 260–271 (2002)Google Scholar
  7. 7.
    Jackson, J.: Principal components and factor analysis: part 1. Journal of Quality Technology 12, 201–213 (1980)Google Scholar
  8. 8.
    Johnson, N., Hogg, D.: Representation and synthesis of behaviour using gaussian mixtures. Image and Vision Computing 20, 889–894 (2002)CrossRefGoogle Scholar
  9. 9.
    Michel, O.: Webots: Professional mobile robot simulation. Journal of Advanced Robotics Systems 1(1), 29–42 (2004)Google Scholar
  10. 10.
    Nehmzov, U.: Quantitative analysis of robot-environment interaction towards ”scientific mobile robotics”. Robotics and Autonomous Systems 44, 55–68 (2003)CrossRefGoogle Scholar
  11. 11.
    Owens, J., Hunter, A., Fletcher, E.: Novelty Detection in Video Surveillance Using Hierarchical Neural Networks. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 1249–1254. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Porikli, F.: Learning object trajectory patterns by spectral clustering. In: IEEE Conference on Multimedia and Expo., vol. 2, pp. 1171–1174. IEEE, Los Alamitos (2004)Google Scholar
  13. 13.
    Remagnino, P., Tan, T., Baker, K.: Agent orientated annotation in model based visual surveillance. In: Sixth International Conference on Computer Vision, p. 857. IEEE Computer Society, Los Alamitos (1998)Google Scholar
  14. 14.
    Sas, C., O’Hare, G.M.P., Reilly, R.G.: A Performance Analysis of Movement Patterns. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 954–961. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Sas, C., O’Hare, G., Reilly, R.: Virtual environment trajectory analysis: a basis for navigational assistance and scene adaptivity. Future Generation Computer Systems 21(7), 1157–1166 (2005)CrossRefGoogle Scholar
  16. 16.
    Schöner, G., Dose, M., Engels, C.: Dynamics of behavior: theory and applications for autonomous robot architecture. Robotics and Autonomous Systems 16, 213–245 (1995)CrossRefGoogle Scholar
  17. 17.
    Smithers, T.: On quantitative performance measures of robot behaviour. Robotics and Autonomous Systems 15, 107–133 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pierre Roduit
    • 1
    • 2
  • Alcherio Martinoli
    • 1
  • Jacques Jacot
    • 2
  1. 1.Swarm-Intelligent System Group (SWIS)École Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.Laboratoire de Production Microtechnique (LPM-IPR)École Polytechnique Fédérale de LausanneLausanneSwitzerland

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