Citazione: A. Amato, V. Di Lecce, V. Piuri, Knowledge Based Video Surveillance System, IEEE/ICCTA 2004 14th International Conference on Computer Theory and Application Alexandria, Egypt, September 28-30, 2004. CD-Rom version.
Abstract: In this work, the authors propose a semantic event detection system based on a neural classifier and suitable for video surveillance. Actual video surveillance systems are composed by a distributed network of active video sensors that present their output to a human operator. The main trouble is that human operators are often unable to monitor the output of a large number of video sensors. The goal of the proposed system is to automatically collect real time information to improve the situational awareness of security providers and decision makers. Focus of the system is the evolution from classical “frame difference” paradigm to the new “known scene -> no alarm” and “unknown scene -> alarm” paradigm, where the meaning of scene is related to spatial-temporal events. The proposed system is able to detect mobile objects in the scene and to class their movements as allowed or disallowed, in order to raise an alarm, also using a video camera mounted on a motorized pan scanner. The results show that the system is able to compensate background changes due to camera motion and to detect mobile objects in the scene.
Keyword: alarm detection, neural classifier, mobile camera