Clustering of vehicle trajectories

Stefan Atev, Grant Miller, Nikolaos P. Papanikolopoulos

Research output: Contribution to journalArticlepeer-review

120 Scopus citations


We present a method that is suitable for clustering of vehicle trajectories obtained by an automated vision system. We combine ideas from two spectral clustering methods and propose a trajectory-similarity measure based on the Hausdorff distance, with modifications to improve its robustness and account for the fact that trajectories are ordered collections of points. We compare the proposed method with two well-known trajectory-clustering methods on a few real-world data sets.

Original languageEnglish (US)
Article number5462900
Pages (from-to)647-657
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number3
StatePublished - Sep 1 2010


  • Clustering of trajectories
  • time-series similarity measures
  • unsupervised learning

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