Destination Flow for Crowd Simulation

  • Stefano Pellegrini
  • Jürgen Gall
  • Leonid Sigal
  • Luc Van Gool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)


We present a crowd simulation that captures some of the semantics of a specific scene by partly reproducing its motion behaviors, both at a lower level using a steering model and at the higher level of goal selection. To this end, we use and generalize a steering model based on linear velocity prediction, termed LTA. From a goal selection perspective, we reproduce many of the motion behaviors of the scene without explicitly specifying them. Behaviors like “wait at the tram stop” or “stroll-around” are not explicitly modeled, but learned from real examples. To this end, we process real data to extract information that we use in our simulation. As a consequence, we can easily integrate real and virtual agents in a mixed reality simulation. We propose two strategies to achieve this goal and validate the results by a user study.


User Study Motion Behavior Virtual Agent Real Trajectory Crowd Simulation 
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

  • Stefano Pellegrini
    • 1
  • Jürgen Gall
    • 2
  • Leonid Sigal
    • 3
  • Luc Van Gool
    • 1
  1. 1.ETH ZurichSwitzerland
  2. 2.MPI for Intelligent SystemsGermany
  3. 3.Disney Research PittsburghUSA

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