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Title:

Vehicle Segmentation and Tracking in the Presence of Occlusions
Cover of Vehicle Segmentation and Tracking in the Presence of Occlusions

Accession Number:

01030689

Record Type:

Component

Availability:

Transportation Research Board Business Office

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Washington, DC 20001 United States
Order URL: http://www.trb.org/Main/Public/Blurbs/157966.aspx

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Order URL: http://worldcat.org/isbn/0309099528

Abstract:

A novel method is presented for visually monitoring a highway automatically when the camera is relatively low to the ground and on the side of the road. In such a case, occlusion and the perspective effects that are due to the heights of the vehicles cannot be ignored. By using a single camera, the system automatically detects and tracks feature points throughout the image sequence, estimates the 3-D world coordinates of the points on the vehicles, and groups those points together to segment and track the individual vehicles. Experimental results on different highways demonstrate the ability of the system to segment and track vehicles even in the presence of severe occlusion and significant perspective changes. By handling perspective effects, the approach overcomes a limitation of commercially available machine vision-based traffic-monitoring systems that are used in many intelligent transportation systems (ITS) applications. Researchers are targeting this system as a step toward a next-generation ITS sensor for automated traffic analysis.

Monograph Accession #:

01030690

Language:

English

Authors:

Kanhere, Neeraj Krantiveer
Birchfield, Stanley T
Sarasua, Wayne A

Pagination:

pp 89-97

Publication Date:

2006

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Issue Number: 1944
Publisher: Transportation Research Board
ISSN: 0361-1981

ISBN:

0309099528

Media Type:

Print

Features:

Figures (4) ; References (24) ; Tables (1)

Subject Areas:

Highways; Operations and Traffic Management; I73: Traffic Control

Files:

TRIS, TRB, ATRI

Created Date:

Mar 3 2006 11:10AM

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