We introduce a novel behavioral model to describe pedestrians motions, which is able to capture sophisticated motion patterns resulting from the mixture of different categories of random trajectories. Due to its simplicity, this model can be learned from video sequences in a totally unsupervised manner through an Expectation-Maximization procedure. When integrated into a complete multi-camera tracking system, it improves the tracking performance in ambiguous situations, compared to a standard ad-hoc isotropic Markovian motion model. Moreover, it can be used to compute a score which characterizes atypical individual motions. Experiments on outdoor video sequences demonstrate both the improvement of tracking performance when compared to a state-of-the-art tracking system and the reliability of the atypical motion detection.