Object Detection in Traffic Videos: A Survey

Hadi Ghahremannezhad, Hang Shi, Chengjun Liu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Traffic video analytics has become one of the core components in the evolution of transportation systems. Artificially intelligent traffic management systems apply computer vision techniques to alleviate the monotony of manually monitoring the video feeds from surveillance cameras. Object detection is the most important step in these systems, and much research has been done on identifying objects in traffic scenes. This paper reviews various algorithms used for object detection in traffic surveillance, in addition to the recent trends and future directions. Based on the approaches used in the related studies, the object detection methods are categorized into motion-based and appearance-based techniques. Each group of techniques is further classified into a number of subcategories and the advantages and disadvantages of each method are finally analyzed. The major challenges, limitations, and potential solutions are also discussed along with the future directions.

Original languageEnglish (US)
Pages (from-to)6780-6799
Number of pages20
JournalIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number7
DOIs
StatePublished - Jul 1 2023

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Automotive Engineering
  • Computer Science Applications

Keywords

  • Object detection
  • deep learning
  • foreground detection
  • instance segmentation
  • traffic surveillance

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