Trajectory Voting and Classification Based on Spatiotemporal Similarity in Moving Object Databases

  • Costas Panagiotakis
  • Nikos Pelekis
  • Ioannis Kopanakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5772)


We propose a method for trajectory classification based on trajectory voting in Moving Object Databases (MOD). Trajectory voting is performed based on local trajectory similarity. This is a relatively new topic in the spatial and spatiotemporal database literature with a variety of applications like trajectory summarization, classification, searching and retrieval. In this work, we have used moving object databases in space, acquiring spatiotemporal 3-D trajectories, consisting of the 2-D geographic location and the 1-D time information. Each trajectory is modelled by sequential 3-D line segments. The global voting method is applied for each segment of the trajectory, forming a local trajectory descriptor. By the analysis of this descriptor the representative paths of the trajectory can be detected, that can be used to visualize a MOD. Our experimental results verify that the proposed method efficiently classifies trajectories and their sub-trajectories based on a robust voting method.


Line Segment Trajectory Segment Vote Function Sparse Bayesian Learning Move Object Database 
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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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Costas Panagiotakis
    • 1
  • Nikos Pelekis
    • 2
  • Ioannis Kopanakis
    • 3
  1. 1.Dept. of Computer ScienceUniversity of CreteGreece
  2. 2.Dept. of InformaticsUniversity of PiraeusGreece
  3. 3.E-Business Intelligence Lab, Dept. of MarketingTechnological Educational Institute of CreteGreece

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