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A computer vision system for the detection and classification of vehicles at urban road intersections

A computer vision system for the detection and classification of vehicles at urban road... The paper presents a real-time vision system to compute traffic parameters by analyzing monocular image sequences coming from pole-mounted video cameras at urban crossroads. The system uses a combination of segmentation and motion information to localize and track moving objects on the road plane, utilizing a robust background updating, and a feature-based tracking method. It is able to describe the path of each detected vehicle, to estimate its speed and to classify it into seven categories. The classification task relies on a model-based matching technique refined by a feature-based one for distinguishing between classes having similar models, like bicycles and motorcycles. The system is flexible with respect to the intersection geometry and the camera position. Experimental results demonstrate robust, real-time vehicle detection, tracking and classification over several hours of videos taken under different illumination conditions. The system is presently under trial in Trento, a 100,000-people town in northern Italy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Pattern Analysis and Applications Springer Journals

A computer vision system for the detection and classification of vehicles at urban road intersections

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References (37)

Publisher
Springer Journals
Copyright
Copyright © 2005 by Springer-Verlag London Limited
Subject
ComputerScience
ISSN
1433-7541
eISSN
1433-755X
DOI
10.1007/s10044-004-0239-9
Publisher site
See Article on Publisher Site

Abstract

The paper presents a real-time vision system to compute traffic parameters by analyzing monocular image sequences coming from pole-mounted video cameras at urban crossroads. The system uses a combination of segmentation and motion information to localize and track moving objects on the road plane, utilizing a robust background updating, and a feature-based tracking method. It is able to describe the path of each detected vehicle, to estimate its speed and to classify it into seven categories. The classification task relies on a model-based matching technique refined by a feature-based one for distinguishing between classes having similar models, like bicycles and motorcycles. The system is flexible with respect to the intersection geometry and the camera position. Experimental results demonstrate robust, real-time vehicle detection, tracking and classification over several hours of videos taken under different illumination conditions. The system is presently under trial in Trento, a 100,000-people town in northern Italy.

Journal

Pattern Analysis and ApplicationsSpringer Journals

Published: Jul 2, 2005

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