We propose a framework for detecting medium-level events referring to intervals of frames of a video stream. The detected events can serve as input for an earlier developed framework detecting high-level surveillance events. More specifically, we first define some specific image processing algorithms to effectively identify and track people and items in frames, and then exploit a previously-defined language based on relational algebra extended by intervals to develop both offline and online algorithms for labeling sequences of frames with descriptors such as 'person A has package X' or 'person B is in car C'. An experimental evaluation carried out on real-world data sets shows promising results in terms of both accuracy and performance.

Labeling the Frames of a Video Stream with Interval Events

Persia F.;
2017

Abstract

We propose a framework for detecting medium-level events referring to intervals of frames of a video stream. The detected events can serve as input for an earlier developed framework detecting high-level surveillance events. More specifically, we first define some specific image processing algorithms to effectively identify and track people and items in frames, and then exploit a previously-defined language based on relational algebra extended by intervals to develop both offline and online algorithms for labeling sequences of frames with descriptors such as 'person A has package X' or 'person B is in car C'. An experimental evaluation carried out on real-world data sets shows promising results in terms of both accuracy and performance.
978-1-5090-4284-5
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/166614
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