In this paper we perform an analysis of human behaviour for people standing in a queue with the aim to discover, in an unsupervised way, ongoing unusual or suspicious activities. The main activity types we focus on are detecting people loitering around the queue and people going against the flow of the queue or undertaking a suspicious path. The proposed approach works by first detecting and tracking moving individuals from a stereo depth map in real time. Activity zones (including queue zones) are then automatically learnt employing a soft computing-based algorithm which takes as input the trajectory of detected mobile objects. Statistical properties on zone occupancy and transition between zones makes it possible to discover abnormalities without the need to learn abnormal models beforehand. The approach has been tested on a dataset realistically representing a border crossing and its environment. The current results suggest that the proposed approach constitutes a robust knowledge discovery tool able to extract queue abnormalities.
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