The detection of events in video streams is a central task in the automatic vision paradigm, and spans het- erogeneous fields of application from the surveillance of the environment, to the analysis of scientific data. Actually, although well captured by intuition, the definition itself of event is somewhat hazy and depending on the specific application of interest. In this work, the approach to the problem of event detection is different in nature. Instead of defining the event and searching for it within the data, a normality space of the scene is built from a chosen learning sequence The event detection algorithm works by projecting any newly acquired image onto the normality space so as to calculate a distance from it that represents the innovation of the new frame, and defines the metric for triggering an event alert.

Building a Normality Space of Events - A PCA Approach to Event Detection

CENEDESE, ANGELO;FREZZA, RUGGERO;GENNARI, GIAMBATTISTA;
2008

Abstract

The detection of events in video streams is a central task in the automatic vision paradigm, and spans het- erogeneous fields of application from the surveillance of the environment, to the analysis of scientific data. Actually, although well captured by intuition, the definition itself of event is somewhat hazy and depending on the specific application of interest. In this work, the approach to the problem of event detection is different in nature. Instead of defining the event and searching for it within the data, a normality space of the scene is built from a chosen learning sequence The event detection algorithm works by projecting any newly acquired image onto the normality space so as to calculate a distance from it that represents the innovation of the new frame, and defines the metric for triggering an event alert.
Proc. of the 3rd International Conference on Computer Vision Theory and Applications
9789898111210
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2444017
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