A vision-based approach to collision prediction at traffic intersections

Stefan Atev, Hemanth Arumugam, Osama Masoud, Ravi Janardan, Nikolaos P Papanikolopoulos

Research output: Contribution to journalArticlepeer-review

104 Scopus citations

Abstract

Monitoring traffic intersections in real time and predicting possible collisions is an important first step towards building an early collision-warning system. We present a vision-based system addressing this problem and describe the practical adaptations necessary to achieve real-time performance. Innovative low-overhead collision-prediction algorithms (such as the one using the time-as-axis paradigm) are presented. The proposed system was able to perform successfully in real time on videos of quarter-video graphics array (VGA) (320 × 240) resolution under various weather conditions. The errors in target position and dimension estimates in a test video sequence are quantified and several experimental results are presented.

Original languageEnglish (US)
Pages (from-to)416-423
Number of pages8
JournalIEEE Transactions on Intelligent Transportation Systems
Volume6
Issue number4
DOIs
StatePublished - Dec 2005

Bibliographical note

Funding Information:
Manuscript received February 20, 2004; revised June 23, 2005. This work was supported by the Minnesota Department of Transportation, the Intelligent Transportation Systems (ITS) Institute at the University of Minnesota, and the National Science Foundation through Grant CMS-0127893. The Associate Editor for this paper was M. Kuwahara.

Keywords

  • Collision prediction
  • Machine vision
  • Real-time systems
  • Tracking
  • Traffic control (transportation)

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