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

101 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 1 2005

Keywords

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

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