This paper addresses the problem of automatically learning
common behaviors from long time observations of a scene of interest,
with the purpose of classifying actions and, possibly, detecting anomalies.
Unsupervised learning is used as an eective way to extract information
from the scene with a very limited intervention of the user. The
method we propose is rather general, but ts very naturally to a videosurveillance
scenario, where the same environment is observed for a long
time, usually from a distance. The experimental analysis is based on
thousands of dynamic events acquired by three-weeks observations of a
single-camera video-surveillance system installed in our department.