  In this paper we describe a video surveillance system able to detect traffic
events in videos acquired by fixed videocameras on highways. The events of
interest consist in a specific sequence of situations that occur in the video,
as for instance a vehicle stopping on the emergency lane. Hence, the detection
of these events requires to analyze a temporal sequence in the video stream. We
compare different approaches that exploit architectures based on Recurrent
Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). A first
approach extracts vectors of features, mostly related to motion, from each
video frame and exploits a RNN fed with the resulting sequence of vectors. The
other approaches are based directly on the sequence of frames, that are
eventually enriched with pixel-wise motion information. The obtained stream is
processed by an architecture that stacks a CNN and a RNN, and we also
investigate a transfer-learning-based model. The results are very promising and
the best architecture will be tested online in real operative conditions.
