An approach to urban traffic state estimation by fusing multisource information
IEEE Transactions on Intelligent Transportation Systems
Learning to recognize video-based spatiotemporal events
IEEE Transactions on Intelligent Transportation Systems
A general active-learning framework for on-road vehicle recognition and tracking
IEEE Transactions on Intelligent Transportation Systems
A channel awareness vehicle detector
IEEE Transactions on Intelligent Transportation Systems
Research collaboration and ITS topic evolution: 10 years at T-ITS
IEEE Transactions on Intelligent Transportation Systems
Anomalous video event detection using spatiotemporal context
Computer Vision and Image Understanding
Vehicle headlights detection using markov random fields
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Vehicle image classification based on edge: features and distances comparison
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
International Journal of Computer Vision
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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.