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OVIS: ontology video surveillance indexing and retrieval system

OVIS: ontology video surveillance indexing and retrieval system Nowadays, the diversity and large deployment of video recorders result in a large volume of video data, whose effective use requires a video indexing process. However, this process generates a major problem consisting in the semantic gap between the extracted low-level features and the ground truth. The ontology paradigm provides a promising solution to overcome this problem. However, no naming syntax convention has been followed in the concept creation step, which constitutes another problem. In this paper, we have considered these two issues and have developed a full video surveillance ontology following a formal naming syntax convention and semantics that addresses queries of both academic research and industrial applications. In addition, we propose an ontology video surveillance indexing and retrieval system (OVIS) using a set of semantic web rule language (SWRL) rules that bridges the semantic gap problem. Currently, the existing indexing systems are essentially based on low-level features and the ontology paradigm is used only to support this process with representing surveillance domain. In this paper, we developed the OVIS system based on the SWRL rules and the experiments prove that our approach leads to promising results on the top video evaluation benchmarks and also shows new directions for future developments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer-Verlag London Ltd.
Subject
Computer Science; Multimedia Information Systems; Information Storage and Retrieval; Information Systems Applications (incl.Internet); Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Computer Science, general
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-017-0133-z
Publisher site
See Article on Publisher Site

Abstract

Nowadays, the diversity and large deployment of video recorders result in a large volume of video data, whose effective use requires a video indexing process. However, this process generates a major problem consisting in the semantic gap between the extracted low-level features and the ground truth. The ontology paradigm provides a promising solution to overcome this problem. However, no naming syntax convention has been followed in the concept creation step, which constitutes another problem. In this paper, we have considered these two issues and have developed a full video surveillance ontology following a formal naming syntax convention and semantics that addresses queries of both academic research and industrial applications. In addition, we propose an ontology video surveillance indexing and retrieval system (OVIS) using a set of semantic web rule language (SWRL) rules that bridges the semantic gap problem. Currently, the existing indexing systems are essentially based on low-level features and the ontology paradigm is used only to support this process with representing surveillance domain. In this paper, we developed the OVIS system based on the SWRL rules and the experiments prove that our approach leads to promising results on the top video evaluation benchmarks and also shows new directions for future developments.

Journal

International Journal of Multimedia Information RetrievalSpringer Journals

Published: Sep 18, 2017

References