Automatic learning of an activity-based semantic scene model

The paper proposes an activity-based semantic model for a scene under visual surveillance. It illustrates methods that allow unsupervised learning of the model from trajectory data derived from automatic visual surveillance cameras. Results are shown for each method. Finally, the benefits of such a model in a visual surveillance system are discussed.