The present work introduces a new method for activity extraction from video. To achieve this, we focus on the modelling of context by developing an algorithm that automatically learns the main activity zones of the observed scene by taking as input the trajectories of detected mobiles. Automatically learning the context of the scene (activity zones) allows first to extract a knowledge on the occupancy of the different areas of the scene. In a second step, learned zones are employed to extract people activities by relating mobile trajectories to the learned zones, in this way, the activity of a person can be summarised as the series of zones that the person has visited. For the analysis of the trajectory, a multi resolution analysis is set such that a trajectory is segmented into a series of tracklets based on changing speed points thus allowing differentiating when people stop to interact with elements of the scene or other persons. Tracklets allow thus to extract behavioural information. Starting and ending tracklet points are fed to a simple yet advantageous incremental clustering algorithm to create an initial partition of the scene. Similarity relations between resulting clusters are modelled employing fuzzy relations. These can then be aggregated with typical soft-computing algebra. A clustering algorithm based on the transitive closure calculation of the fuzzy relations allows building the final structure of the scene. To allow for incremental learning and update of activity zones (and thus people activities), fuzzy relations are defined with on-line learning terms. We present results obtained on real videos from different activity domains.