Clustering of Vehicle Trajectories

被引：171

作者：

Atev, Stefan ^{[1
,2
]}

Miller, Grant ^{[1
]}

Papanikolopoulos, Nikolaos P. ^{[3
]}

机构：

[1] Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA

[2] Vital Images Inc, Minnetonka, MN 55343 USA

[3] Univ Minnesota, Dept Comp Sci & Engn, Ctr Transportat Studies, Minneapolis, MN 55455 USA

来源：

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS | 2010年 / 11卷 / 03期

基金：

美国国家科学基金会;

关键词：

Clustering of trajectories; time-series similarity measures; unsupervised learning; ALGORITHMS; PREDICTION; TRACKING;

D O I：

10.1109/TITS.2010.2048101

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

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.

引用

页码：647 / 657

页数：11

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