Clustering of vehicle trajectories

Stefan Atev; Grant Miller; Nikolaos P. Papanikolopoulos

doi:10.1109/TITS.2010.2048101

Clustering of vehicle trajectories

Stefan Atev, Grant Miller, Nikolaos P. Papanikolopoulos

Computer Science and Engineering

Research output: Contribution to journal › Article › peer-review

166 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 language	English (US)
Article number	5462900
Pages (from-to)	647-657
Number of pages	11
Journal	IEEE Transactions on Intelligent Transportation Systems
Volume	11
Issue number	3
DOIs	https://doi.org/10.1109/TITS.2010.2048101
State	Published - 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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10.1109/TITS.2010.2048101

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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.",

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Clustering of vehicle trajectories

Abstract

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