@inproceedings{e1654df72b5546758eaf5b2744de26bc,
title = "A collision prediction system for traffic intersections",
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.",
keywords = "Collision prediction, Machine vision, Real-time systems, Tracking, Traffic control (transportation)",
author = "Stefan Atev and Osama Masoud and Ravi Janardan and Nikos Papanikolopoulos",
year = "2005",
doi = "10.1109/IROS.2005.1545407",
language = "English (US)",
isbn = "0780389123",
series = "2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS",
publisher = "IEEE Computer Society",
pages = "169--174",
booktitle = "2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS",
}