We address the discovery of typical activities in video stream contents and its exploitation for estimating the abnormality levels of these streams. Such estimates can be used to select the most interesting cameras to show to a human operator. Our contributions come from the following facets: i) the method is fully unsupervised and learns the activities from long term data; ii) the method is scalable and can efficiently handle the information provided by multiple un-calibrated cameras, jointly learning activities shared by them if it happens to be the case (e.g. when they have overlapping fields of view); iii) unlike previous methods which were mainly applied to structured urban traffic scenes, we show that ours performs well on videos from a metro environment where human activities are only loosely constrained.