Clustering of vehicle trajectories

Stefan Atev, Grant Miller, Nikolaos P Papanikolopoulos

Research output: Contribution to journalArticle

89 Citations (Scopus)

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 1 2010

Fingerprint

Trajectories

Keywords

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

Cite this

Clustering of vehicle trajectories. / Atev, Stefan; Miller, Grant; Papanikolopoulos, Nikolaos P.

In: IEEE Transactions on Intelligent Transportation Systems, Vol. 11, No. 3, 5462900, 01.09.2010, p. 647-657.

Research output: Contribution to journalArticle

@article{e1abf5d786bc41e7a672a28c2e49f554,
title = "Clustering of vehicle trajectories",
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.",
keywords = "Clustering of trajectories, time-series similarity measures, unsupervised learning",
author = "Stefan Atev and Grant Miller and Papanikolopoulos, {Nikolaos P}",
year = "2010",
month = "9",
day = "1",
doi = "10.1109/TITS.2010.2048101",
language = "English (US)",
volume = "11",
pages = "647--657",
journal = "IEEE Intelligent Transportation Systems Magazine",
issn = "1524-9050",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

TY - JOUR

T1 - Clustering of vehicle trajectories

AU - Atev, Stefan

AU - Miller, Grant

AU - Papanikolopoulos, Nikolaos P

PY - 2010/9/1

Y1 - 2010/9/1

N2 - 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.

AB - 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.

KW - Clustering of trajectories

KW - time-series similarity measures

KW - unsupervised learning

UR - http://www.scopus.com/inward/record.url?scp=77956338953&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956338953&partnerID=8YFLogxK

U2 - 10.1109/TITS.2010.2048101

DO - 10.1109/TITS.2010.2048101

M3 - Article

VL - 11

SP - 647

EP - 657

JO - IEEE Intelligent Transportation Systems Magazine

JF - IEEE Intelligent Transportation Systems Magazine

SN - 1524-9050

IS - 3

M1 - 5462900

ER -