By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
C. Sacchi ; Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy ; C. Regazzoni ; G. Vernazza

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

Published in:

Image Analysis and Processing, 2001. Proceedings. 11th International Conference on

Date of Conference:

26-28 Sep 2001