Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Hierarchical Model for Real Time Simulation of Virtual Human Crowds
IEEE Transactions on Visualization and Computer Graphics
Extraction and Clustering of Motion Trajectories in Video
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions
Transportation Science
Learning semantic scene models from observing activity in visual surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-Organizing Maps for the Automatic Interpretation of Crowd Dynamics
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Motion estimation with edge continuity constraint for crowd scene analysis
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
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The Ambient Intelligence (AmI) paradigm requires a robust interpretation of people actions and behaviour and a way for automatically generating persistent spatial-temporal models of recurring events. This paper describes a relatively inexpensive technique that does not require the use of conventional trackers to identify the main paths of highly cluttered scenes, approximating them with spline curves. An AmI system could easily make use of the generated model to identify people who do not follow prefixed paths and warn them. Security, safety, rehabilitation are potential application areas. The model is evaluated against new data of the same scene.