Road Weather Condition Estimation Using Fixed and Mobile Based Cameras

Koray Ozcan, Anuj Sharma, Skylar Knickerbocker, Jennifer Merickel, Neal Hawkins, Matthew Rizzo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Automated interpretation and understanding of the driving environment using image processing is a challenging task, as most current vision-based systems are not designed to work in dynamically-changing and naturalistic real-world settings. For instance, road weather condition classification using a camera is a challenge due to high variance in weather, road layout, and illumination conditions. Most transportation agencies, within the U.S., have deployed some cameras for operational awareness. Given that weather related crashes constitute 22% of all vehicle crashes and 16% of crash fatalities, this study proposes using these same cameras as a source for estimating roadway surface condition. The developed model is focused on three road surface conditions resulting from weather including: Clear (clear/dry), Rainy-Wet (rainy/slushy/wet), and Snow (snow-covered/partially snow-covered). The camera sources evaluated are both fixed Closed-circuit Television (CCTV) and mobile (snow plow dash-cam). The results are promising; with an achieved 98.57% and 77.32% road weather classification accuracy for CCTV and mobile cameras, respectively. Proposed classification method is suitable for autonomous selection of snow plow routes and verification of extreme road conditions on roadways.

Original languageEnglish (US)
Title of host publicationAdvances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC
EditorsSupriya Kapoor, Kohei Arai
PublisherSpringer Verlag
Pages192-204
Number of pages13
ISBN (Print)9783030177942
DOIs
StatePublished - Jan 1 2020
EventComputer Vision Conference, CVC 2019 - Las Vegas, United States
Duration: Apr 25 2019Apr 26 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume943
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceComputer Vision Conference, CVC 2019
CountryUnited States
CityLas Vegas
Period4/25/194/26/19

Keywords

  • CCTV
  • Mobile camera
  • Neural networks
  • Road weather classification
  • Scene classification
  • VGG16

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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  • Cite this

    Ozcan, K., Sharma, A., Knickerbocker, S., Merickel, J., Hawkins, N., & Rizzo, M. (2020). Road Weather Condition Estimation Using Fixed and Mobile Based Cameras. In S. Kapoor, & K. Arai (Eds.), Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC (pp. 192-204). (Advances in Intelligent Systems and Computing; Vol. 943). Springer Verlag. https://doi.org/10.1007/978-3-030-17795-9_14