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Title:

Vehicle Detection from Satellite Images: Intensity-, Chromaticity-, and Lane-Based Method

Accession Number:

01137542

Record Type:

Component

Availability:

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Washington, DC 20001 United States
Order URL: http://trb.org/Main/Blurbs/Information...ographic_Information_Systems_162392.aspx

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Order URL: http://worldcat.org/isbn/9780309126205

Abstract:

Hurricanes or other natural disasters often result in damage to and disruption of transportation systems that are critical to relief and recovery efforts. For this reason, it is important for the general public and elected officials to know the operating conditions of a transportation system as soon as possible after a disaster. The goal of this study was to develop methods for rapidly determining the operating conditions of roadways from satellite images by detecting the presence of debris and the blockage of roads as well as vehicle flows. This paper describes efforts for automatically delineating traffic lanes, a prerequisite for determining blockages by debris, and for detecting vehicles from satellite images. Instead of relying on pixel intensity alone, this study attempted to seek additional information to improve vehicle detection rates by combining information on traffic lane locations and the color tones of vehicles. The results show that the proposed method performs well in automatic traffic lane delineation and vehicle detection.

Monograph Accession #:

01141653

Report/Paper Numbers:

09-2993

Language:

English

Authors:

Hongbo, Chi
Lu, Chenxi
Zhao, Fang
Shen, L David

Pagination:

pp 109-117

Publication Date:

2009

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Issue Number: 2105
Publisher: Transportation Research Board
ISSN: 0361-1981

ISBN:

9780309126205

Media Type:

Print

Features:

Figures (7) ; References (17) ; Tables (2)

Subject Areas:

Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning

Files:

TRIS, TRB, ATRI

Created Date:

Jan 30 2009 7:22PM

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