It is well known that video cameras provide one of the richest, and most promising sources of information about people's movements. New technologies which combine video understanding and data-mining can analyse people's behaviour in an efficient way by extracting their trajectories and identifying the main movement flows within a scene equipped with video cameras. For instance, we are designing an activity recognition framework which can monitor people's behaviour in an unsupervised manner. For each observed person, the framework extracts a set of space-time trajectory features describing his/her global position within the monitored scene and the motion of his/her body parts. Based on trajectory clustering, human information is gathered in a new feature that we call Perceptual Feature Chunks (PFC). The set of PFC is used to automatically learn the particular regions of a given scene where important activities occur. We call this set of learned scene regions the topology. Based on a k-means algorithm, a clustering procedure over the PFCs is performed in order to construct three topology layers, organized from coarsest to finest. Using topologies and PFCs, we are able to break the video into a set of small events or primitive events (PE), each of which has a semantic meaning. The sequences of PEs, and three layers of topology, are used to construct a hierarchical model with three granularity levels of activity.