A collision prediction system for traffic intersections

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

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

16 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 the general vision methods used in a system addressing this problem and describe the practical adaptations necessary to achieve real-time performance. A novel method for three-dimensional vehicle size estimation is presented. We also describe a method for target localization in real-world coordinates which allows for sequential incorporation of measurements from multiple cameras into a single target's state vector. Additionally, a fast implementation of a false-positive reduction method for the foreground pixel masks is developed. Finally, a low-overhead collision prediction algorithm using the time-as-axis paradigm is presented. The proposed system was able to perform in real-time on videos of quarter-VGA (320×240) resolution. The errors in target position and dimension estimates in a test video sequence are quantified.

Original languageEnglish (US)
Title of host publication2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
PublisherIEEE Computer Society
Pages169-174
Number of pages6
ISBN (Print)0780389123, 9780780389120
DOIs
StatePublished - 2005

Publication series

Name2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS

Keywords

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

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