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In this paper we propose a novel method to analyze trajectories in surveillance scenarios relying on automatically learned Context-Free Grammars. Given a training corpus of trajectories associated to a set of actions, an initial processing is carried out to extract the syntactical structure of the activities; then, the rules characterizing different behaviors are retrieved and coded as CFG models. The classification of the new trajectories vs the learned templates is performed through a parsing engine allowing the online recognition as well as the detection of nested activities. The proposed system has been validated in the framework of assisted living applications. The obtained results demonstrate the capability of the system in recognizing activity patterns in different configurations, also in presence of noise.