A Non-parametric Hierarchical Model to Discover Behavior Dynamics from Tracks

  • Julian F. P. Kooij
  • Gwenn Englebienne
  • Dariu M. Gavrila
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)


We present a novel non-parametric Bayesian model to jointly discover the dynamics of low-level actions and high-level behaviors of tracked people in open environments. Our model represents behaviors as Markov chains of actions which capture high-level temporal dynamics. Actions may be shared by various behaviors and represent spatially localized occurrences of a person’s low-level motion dynamics using Switching Linear Dynamics Systems. Since the model handles real-valued features directly, we do not lose information by quantizing measurements to ‘visual words’ and can thus discover variations in standing, walking and running without discrete thresholds. We describe inference using Gibbs sampling and validate our approach on several artificial and real-world tracking datasets. We show that our model can distinguish relevant behavior patterns that an existing state-of-the-art method for hierarchical clustering cannot.


Anomaly Detection Latent Dirichlet Allocation Dynamic Time Warping Behavior Class Test Track 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Julian F. P. Kooij
    • 1
  • Gwenn Englebienne
    • 1
  • Dariu M. Gavrila
    • 1
  1. 1.Intelligent Systems LaboratoryUniversity of AmsterdamThe Netherlands

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