Yang, Jidong J.; Ying Wang; and Chih-Cheng Hung
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This study explored the art of deep learning, multiple-object detection and tracking, and performed testing in the domain of traffic conflict monitoring and assessment. The study used the existing traffic cameras installed at major intersections to develop and deploy smart algorithms to scan live images and extract traffic conflicts in real time. For this purpose, an Artificial Intelligence (AI) enhanced computational system was developed to automate the detection and quantification of traffic conflict events as they occur in real time using traffic cameras currently installed by transportation agencies. Tests on simulated and actual video images demonstrated the promise of the proposed approach for detecting and quantifying traffic conflict events.

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Prepared by Georgia Pavement & Traffic Research Center for Georgia DOT
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Monitoring and Assessing Traffic Safety at Signalized Intersections Using Live Video Images
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