Learning traffic patterns at intersections by spectral clustering of motion trajectories

被引：50

作者：

Atev, Stefan ^{[1
]}

Masoud, Osama ^{[1
]}

Papanikolopoulos, Nikos ^{[1
]}

机构：

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

来源：

2006 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-12 | 2006年

基金：

美国国家科学基金会;

关键词：

D O I：

10.1109/IROS.2006.282362

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We address the problem of automatically learning the layout of a traffic intersection from trajectories of vehicles obtained by a vision tracking system. We present a similarity measure which is suitable for use with spectral clustering in problems that emphasize spatial distinctions between vehicle trajectories. The robustness of the method to small perturbations and its sensitivity to the choice of parameters are evaluated using real-world data.

引用

页码：4851 / +

页数：2

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