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Title:

Automatic Vehicle Counting and Tracking in Aerial Video Feeds using Cascade Region-based Convolutional Neural Networks and Feature Pyramid Networks

Accession Number:

01764389

Record Type:

Component

Availability:

Find a library where document is available


Order URL: http://worldcat.org/issn/03611981

Abstract:

Unmanned aerial vehicles, or drones, are poised to solve many problems associated with data collection in complex urban environments. Drones are easy to deploy, have a great ability to move and explore the environment, and are relatively cheaper than other data collection methods. This study investigated the use of Cascade Region-based convolutional neural network (R-CNN) networks to enable automatic vehicle counting and tracking in aerial video streams.The presented technique combines feature pyramid networks and a Cascade R-CNN architecture to enable accurate detection and classification of vehicles.The paper discusses the implementation and evaluation of the detection and tracking techniques and highlights their advantages when they are used to collect traffic data.

Supplemental Notes:

© National Academy of Sciences: Transportation Research Board 2021.

Report/Paper Numbers:

TRBAM-21-03070

Language:

English

Authors:

Youssef, Yomna
Elshenawy, Mohamed

Pagination:

pp 304-317

Publication Date:

2021-8

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Volume: 2675
Issue Number: 8
Publisher: Sage Publications, Incorporated
ISSN: 0361-1981
EISSN: 2169-4052
Serial URL: http://journals.sagepub.com/home/trr

Media Type:

Digital/other

Features:

Figures; References (49) ; Tables

Subject Areas:

Data and Information Technology; Highways; Operations and Traffic Management

Files:

TRIS, TRB, ATRI

Created Date:

Dec 23 2020 11:25AM