JOURNAL ARTICLE

A computer vision‐based deep learning model to detect wrong‐way driving using pan–tilt–zoom traffic cameras.

  • Published In: Computer-Aided Civil & Infrastructure Engineering, 2023, v. 38, n. 1. P. 119 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Haghighat, Arya; Sharma, Anuj 3 of 3

Abstract

Hundreds of fatal accidents occur each year due to wrong‐way driving (WWD). Although several methods have been developed to detect WWD using existing closed‐circuit television (CCTV) data, they all require manual recalibration whenever a camera rotates, and are thus not scalable across statewide CCTV networks. This paper, therefore, proposes an end‐to‐end deep‐learning‐based model that considers camera orientation as a variable, detecting camera rotation automatically and learning new decision criteria accordingly using a neural network model. We show that our proposed solution can detect WWD with a precision of 0.99 and a recall of 0.97. Due to its cheap computational cost and high error tolerance, our solution is easily scalable for statewide surveillance on a real‐time basis to help decision‐makers reduce fatalities due to WWD. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Computer-Aided Civil & Infrastructure Engineering. 2023/01, Vol. 38, Issue 1, p119
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:1093-9687
  • DOI:10.1111/mice.12819
  • Accession Number:160853917
  • Copyright Statement:Copyright of Computer-Aided Civil & Infrastructure Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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