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Learning, modeling, and classification of vehicle track patterns from live video

By Brendan Tran Morris, Student Member, Mohan Manubhai Trivedi and Senior Member

Abstract

Abstract—This paper presents two different types of visual activity analysis modules based on vehicle tracking. The highway monitoring module accurately classifies vehicles into eight different types and collects traffic flow statistics by leveraging tracking information. These statistics are continuously accumulated to maintain daily highway models that are used to categorize traffic flow in real time. The path modeling block is a more general analysis tool that learns the normal motions encountered in a scene in an unsupervised fashion. The spatiotemporal motion characteristics of these motion paths are encoded by a hidden Markov model. With the path definitions, abnormal trajectories are detected and future intent is predicted. These modules add realtime situational awareness to highway monitoring for high-level activity and behavior analysis. Index Terms—Anomaly detection, comparative flow analysis, highway efficiency, real-time tracking analysis, trajectory learning and prediction, vehicle type classification. I

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.1529
Provided by: CiteSeerX
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