Learning, Modeling, and Classification of Vehicle Track Patterns from Live Video

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 ac...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2008-09, Vol.9 (3), p.425-437
Main Authors: Morris, B.T., Trivedi, M.M.
Format: Article
Language:English
Subjects:
Anomaly detection
Applied sciences
Artificial intelligence
Classification
comparative flow analysis
Computer science
control theory
systems
Exact sciences and technology
Hidden Markov models
highway efficiency
Highways
Layout
Mathematical models
Modules
Monitoring
Motion analysis
Pattern analysis
Pattern recognition. Digital image processing. Computational geometry
real-time tracking analysis
Road transportation
Road vehicles
Spatiotemporal phenomena
Statistics
Tracking
Traffic control
trajectory learning and prediction
vehicle type classification
Vehicles
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Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2008.922970