Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning variable-length Markov models of behavior
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Guest Introduction: The Changing Shape of Computer Vision in the Twenty-First Century
International Journal of Computer Vision
International Journal of Computer Vision
Real-time closed-world tracking
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Moving Target Classification and Tracking from Real-time Video
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Ontology-Driven Bayesian Networks for Dynamic Scene Understanding
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
Layered representations for learning and inferring office activity from multiple sensory channels
Computer Vision and Image Understanding - Special issue on event detection in video
An ontology-based information retrieval system
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Vs-star: A visual interpretation system for visual surveillance
Pattern Recognition Letters
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The analysis of events in dynamic scenes has become an important and challenging problem increasingly in recent years. Events can be considered as obvious changes of important features with semantic meanings. From this viewpoint, the fundamental task of events analysis is to extract semantically meaningful changes and associate all of these basic motion patterns and changes with relevant visual concepts of moving objects in dynamic scenes. In this paper, we propose a method to extract lower level motion patterns and associate them with visual concepts respectively in a well-defined structure. Furthermore we also analyze latent spatial-temporal relationships among these basic visual concepts for event modeling and analysis. Finally, we present experimental results which prove the effectiveness of our approach on some real-world videos of dynamic scenes.