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Proceedings Paper

Real-time high-level video understanding using data warehouse
Author(s): Bruno Lienard; Xavier Desurmont; Bertrand Barrie; Jean-Francois Delaigle
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Paper Abstract

High-level Video content analysis such as video-surveillance is often limited by computational aspects of automatic image understanding, i.e. it requires huge computing resources for reasoning processes like categorization and huge amount of data to represent knowledge of objects, scenarios and other models. This article explains how to design and develop a "near real-time adaptive image datamart", used, as a decisional support system for vision algorithms, and then as a mass storage system. Using RDF specification as storing format of vision algorithms meta-data, we can optimise the data warehouse concepts for video analysis, add some processes able to adapt the current model and pre-process data to speed-up queries. In this way, when new data is sent from a sensor to the data warehouse for long term storage, using remote procedure call embedded in object-oriented interfaces to simplified queries, they are processed and in memory data-model is updated. After some processing, possible interpretations of this data can be returned back to the sensor. To demonstrate this new approach, we will present typical scenarios applied to this architecture such as people tracking and events detection in a multi-camera network. Finally we will show how this system becomes a high-semantic data container for external data-mining.

Paper Details

Date Published: 15 February 2006
PDF: 14 pages
Proc. SPIE 6063, Real-Time Image Processing 2006, 606305 (15 February 2006); doi: 10.1117/12.642942
Show Author Affiliations
Bruno Lienard, Multitel A.S.B.L. (Belgium)
Xavier Desurmont, Multitel A.S.B.L. (Belgium)
Bertrand Barrie, Multitel A.S.B.L. (Belgium)
Jean-Francois Delaigle, Multitel A.S.B.L. (Belgium)

Published in SPIE Proceedings Vol. 6063:
Real-Time Image Processing 2006
Nasser Kehtarnavaz; Phillip A. Laplante, Editor(s)

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