A neural network-based image processing system for detection of vandal acts in unmanned railway environments

Lately, the interest in advanced video-based surveillance applications has been increasing. This is especially true in the field of urban railway transport where video-based surveillance can be exploited to face many relevant security aspects (e.g. vandalism, overcrowding, abandoned object detection etc.). This paper aims at investigating 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 considered the use of a neural network-based classifier for detecting vandal behavior in metro stations. The achieved results show that the classifier achieves very good performance even in the presence of high scene complexity.