, Volume 11, Issue 2, pp 195–215 | Cite as

Efficient Detection of Patterns in 2D Trajectories of Moving Points

  • Joachim Gudmundsson
  • Marc van KreveldEmail author
  • Bettina Speckmann


Moving point object data can be analyzed through the discovery of patterns in trajectories. We consider the computational efficiency of detecting four such spatio-temporal patterns, namely flock, leadership, convergence, and encounter, as defined by Laube et al., Finding REMO—detecting relative motion patterns in geospatial lifelines, 201–214, (2004). These patterns are large enough subgroups of the moving point objects that exhibit similar movement in the sense of direction, heading for the same location, and/or proximity. By the use of techniques from computational geometry, including approximation algorithms, we improve the running time bounds of existing algorithms to detect these patterns.


computational geometry motion patterns tracking data approximation algorithms data mining 


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Joachim Gudmundsson
    • 1
  • Marc van Kreveld
    • 2
    Email author
  • Bettina Speckmann
    • 3
  1. 1.NICTASydneyAustralia
  2. 2.Department of Information and Computing SciencesUtrecht UniversityUtrechtThe Netherlands
  3. 3.Department of Mathematics and Computer ScienceTU EindhovenEindhovenThe Netherlands

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