Use of neural networks for behaviour understanding in railway transport monitoring applications

Interest for advanced video-based surveillance applications has been growing rapidly. This is especially true in the field of railway urban transport where video-based surveillance can be exploited to face many relevant security aspects (e.g. vandal acts, overcrowding situations, abandoned object detection, etc.). This paper investigates an open problem in the implementation of video-based surveillance systems for transport applications, i.e.: the implementation of reliable image understanding modules in order to recognize dangerous situations with reduced false alarm and misdetection rates. We consider the use of a neural network-based classifier for detecting the behavior of vandals in metro stations. The achieved results show that the classifier choice mentioned above allows one to achieve very good performances also in the presence of high scene complexity