Clustering of vehicle trajectories

Stefan Atev, Grant Miller, Nikolaos P. Papanikolopoulos

Research output: Contribution to journalArticlepeer-review

117 Scopus citations

Abstract

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
Volume11
Issue number3
DOIs
StatePublished - Sep 2010

Bibliographical note

Funding Information:
Manuscript received April 13, 2008; revised April 11, 2008, June 13, 2009, December 26, 2009, and March 13, 2010; accepted March 16, 2010. Date of publication May 10, 2010; date of current version September 3, 2010. This work was supported in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under Contract 911NF-08-1-0463 (Proposal 55111-CI), by the National Science Foundation through under Grant IIS-0219863, Grant IIP-0443945, Grant IIP-0726109, Grant CNS-0708344, and Grant IIP-0934327, by the Minnesota Department of Transportation, and by the ITS Institute at the University of Minnesota. The Associate Editor for this paper was Q. Ji.

Keywords

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

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