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This paper presents a novel approach to describe traffic accident events at intersections in human-understandable way using automated video processing techniques. The research mainly proposes a new technique for video-based traffic accident analysis by extracting abnormal event characteristics at intersections. The approach relies on learning normal traffic(More)
This paper proposes a novel traffic event classification approach using event severities at intersections. The proposed system basically learns normal and common traffic flow by clustering vehicle trajectories. Common vehicle routes are generated by implementing trajectory clustering with Continuous Hidden Markov Model. Vehicle abnormality is detected by(More)
The purpose of this work is to investigate the severity characteristics of abnormal events at intersections by using video processing techniques and statistical deviation analysis methods. In order to detect the abnormal events, trajectory of normal vehicle motions are clustered and common route models are learned by Continuous Hidden Markov Model. In the(More)
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