The main purpose of video-based event recognition is to interpret activities or behaviors within video sequences, in order to detect and isolate specific events, which have to be readily recognized and prompted to the people responsible for their monitoring. In this paper, we present a knowledge representation framework and a system for automatic video surveillance, which analyzes record scenes in order to detect the occurrence of specific events defined as targets. The framework, named Elements and Descriptors of Context and Action Representations (EDCAR), enables the representation of relevant elements, general descriptors of the context, and actions that have to be captured, including the definition of action compositions and sequences, in order to monitor and recognize abnormal situations. EDCAR and the associated system also support video summarization of relevant scenes, providing an inference engine to handle complex queries. They have been used experimentally on several video surveillance scenarios, which enabled us to prove their effectiveness with respect to similar solutions described in the literature. (C) 2019 Elsevier Ltd. All rights reserved.

EDCAR: A knowledge representation framework to enhance automatic video surveillance

Caruccio, Loredana
;
Polese, Giuseppe;Tortora, Genoveffa;
2019-01-01

Abstract

The main purpose of video-based event recognition is to interpret activities or behaviors within video sequences, in order to detect and isolate specific events, which have to be readily recognized and prompted to the people responsible for their monitoring. In this paper, we present a knowledge representation framework and a system for automatic video surveillance, which analyzes record scenes in order to detect the occurrence of specific events defined as targets. The framework, named Elements and Descriptors of Context and Action Representations (EDCAR), enables the representation of relevant elements, general descriptors of the context, and actions that have to be captured, including the definition of action compositions and sequences, in order to monitor and recognize abnormal situations. EDCAR and the associated system also support video summarization of relevant scenes, providing an inference engine to handle complex queries. They have been used experimentally on several video surveillance scenarios, which enabled us to prove their effectiveness with respect to similar solutions described in the literature. (C) 2019 Elsevier Ltd. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4726127
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