Vehicle detection in intelligent transportation systems and its applications under varying environments: A review

Zi Yang, Shuk Ching Lilian Cheng

Research output: Journal article publicationReview articleAcademic researchpeer-review

83 Citations (Scopus)


Robust and efficient vehicle detection in monocular vision is an important task in Intelligent Transportation Systems. With the development of computer vision techniques and consequent accessibility of video image data, new applications have been enabled to on-road vehicle detection algorithms. This paper provides a review of the literature in vehicle detection under varying environments. Due to the variability of on-road driving environments, vehicle detection may face different problems and challenges. Therefore, many approaches have been proposed, and can be categorized as appearance-based methods and motion-based methods. In addition, special illumination, weather and driving scenarios are discussed in terms of methodology and quantitative evaluation. In the future, efforts should be focused on robust vehicle detection approaches for various on-road conditions.
Original languageEnglish
Pages (from-to)143-154
Number of pages12
JournalImage and Vision Computing
Publication statusPublished - 1 Jan 2018


  • Computer vision
  • Intelligent Transportation Systems
  • Traffic surveillance
  • Varying environments
  • Vehicle detection

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition

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