A collision prediction system for traffic intersections

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

doi:10.1109/IROS.2005.1545407

A collision prediction system for traffic intersections

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

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

19 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 language	English (US)
Title of host publication	2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
Publisher	IEEE Computer Society
Pages	169-174
Number of pages	6
ISBN (Print)	0780389123, 9780780389120
DOIs	https://doi.org/10.1109/IROS.2005.1545407
State	Published - 2005

Publication series

Name	2005 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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Atev, S., Masoud, O., Janardan, R., & Papanikolopoulos, N. P. (2005). A collision prediction system for traffic intersections. In 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (pp. 169-174). Article 1545407 (2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS). IEEE Computer Society. https://doi.org/10.1109/IROS.2005.1545407

A collision prediction system for traffic intersections. / Atev, Stefan; Masoud, Osama; Janardan, Ravi et al.
2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. IEEE Computer Society, 2005. p. 169-174 1545407 (2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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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.",

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AB - 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.

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A collision prediction system for traffic intersections

Abstract

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