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Title:

Automated Region-Based Vehicle Conflict Detection Using Computer Vision Techniques

Accession Number:

01557447

Record Type:

Component

Availability:

Transportation Research Board Business Office

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Washington, DC 20001 United States

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Order URL: http://worldcat.org/isbn/9780309369084

Abstract:

An approach for vehicle conflict analysis based on three-dimensional (3-D) vehicle detection is presented. Techniques for quantitative conflict measurements often use a point trajectory representation for vehicles. More accurate conflict measurement can be facilitated with a region-based vehicle representation instead. This paper describes a computer vision approach for extracting vehicle trajectories from video sequences. The method relied on a fusion of background subtraction and feature-based tracking to provide a three-dimensional (3-D) cuboid representation of the vehicle. Standard conflict measures, including time to collision and postencroachment time, were computed with the use of the 3-D cuboid vehicle representations. The use of these conflict measures was demonstrated on a challenging data set of video footage. Results showed that the region-based representation could provide more precise calculation of traffic conflict indicators compared with approaches based on a point representation.

Monograph Accession #:

01582941

Report/Paper Numbers:

15-4892

Language:

English

Authors:

Li, Jinling
Liu, Yuhao
Tageldin, Ahmed
Zaki, Mohamed H
Mori, Greg
Sayed, Tarek

Pagination:

pp 49–59

Publication Date:

2015

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Issue Number: 2528
Publisher: Transportation Research Board
ISSN: 0361-1981

ISBN:

9780309369084

Media Type:

Print

Features:

Figures (8) ; Photos; References (30) ; Tables (1)

Subject Areas:

Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; Safety and Human Factors; I85: Safety Devices used in Transport Infrastructure

Files:

TRIS, TRB, ATRI

Created Date:

Dec 30 2014 1:38PM

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