OVIS: ontology video surveillance indexing ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
OVIS: ontology video surveillance indexing and retrieval system
Author(s) :
Kazi Tani, Mohammed Yassine [Auteur]
Université d'Oran 1 Ahmed Ben Bella [Oran]
Ghomari, Abdelghani [Auteur]
Université d'Oran 1 Ahmed Ben Bella [Oran]
Lablack, Adel [Auteur]
FOX MIIRE [LIFL]
Bilasco, Ioan Marius [Auteur]
FOX MIIRE [LIFL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université d'Oran 1 Ahmed Ben Bella [Oran]
Ghomari, Abdelghani [Auteur]
Université d'Oran 1 Ahmed Ben Bella [Oran]
Lablack, Adel [Auteur]
FOX MIIRE [LIFL]
Bilasco, Ioan Marius [Auteur]
FOX MIIRE [LIFL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Journal title :
International Journal of Multimedia Information Retrieval
Pages :
295-316
Publisher :
Springer
Publication date :
2017
ISSN :
2192-6611
English keyword(s) :
Video Surveillance Ontology
Video-Indexing
Crowdsourced events
Semantic-Gap
Naming Syntax Convention
OVIS System
SWRL Rules
Video-Indexing
Crowdsourced events
Semantic-Gap
Naming Syntax Convention
OVIS System
SWRL Rules
HAL domain(s) :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
English abstract : [en]
Nowadays, the diversity and large deployment of video recorders results in a large volume of video data, whose effective use requires a video indexing process. However, this process generates a major problem consisting in ...
Show more >Nowadays, the diversity and large deployment of video recorders results 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.Show less >
Show more >Nowadays, the diversity and large deployment of video recorders results 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.Show less >
Language :
Anglais
Popular science :
Non
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