EDCAR: A knowledge representation framework to enhance automatic video surveillance
| Autor: | Genoveffa Tortora, Daniele Iannone, Loredana Caruccio, Giuseppe Polese |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Knowledge representation and reasoning Computer science Video surveillance Knowledge representation framework ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Context (language use) 02 engineering and technology Space (commercial competition) Event recognition Action composition 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering SPACE Inference engine Information retrieval Video scenario RECOGNITION General Engineering Representation (systemics) Automatic summarization WEB Computer Science Applications Action (philosophy) 020201 artificial intelligence & image processing |
| Zdroj: | Expert Systems with Applications. 131:190-207 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2019.04.031 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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