OVIS: ontology video surveillance indexing and retrieval system

Tani, Mohammed Yassine Kazi; Ghomari, Abdelghani; Lablack, Adel; Bilasco, Ioan Marius

doi:10.1007/s13735-017-0133-z

Cited by 18 publications

(2 citation statements)

References 37 publications

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“…Consequently, a suitable approach to tackle these issues is required and that approach could be through suitable abstraction technologies, such as semantic content representation or context-aware perceptual modeling or through ontologies. In recent years, context-aware, ontological-based, perceptual modeling approaches have been adopted in the video surveillance application [20][21] (see Section 3) and information security [22][23][24] domains. Ontologies enable the content description of basic categories within the domain and relations among them to make them machine understandable [25].…”

Section: Contextmentioning

confidence: 99%

“…However, the ontology was not integrated with algorithms for intelligent detection, tracking, and recognition of events. In [21], Tani et al presented a Semantic Web Rule Language (SWRL)-based ontology to bridge the semantic-gap problem and detect a single or multiple objects events in surveillance video. In [49] Calavia et al also proposed an intelligent video surveillance ontology system to analyze object movements and recognize abnormal circumstances.…”

Section: B Ontologies In Video Surveillancementioning

confidence: 99%

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MuLViS: Multi-Level Encryption Based Security System for Surveillance Videos

et al. 2020

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Video Surveillance (VS) systems are commonly deployed for real-time abnormal event detection and autonomous video analytics. Video captured by surveillance cameras in real-time often contains identifiable personal information, which must be privacy protected, sometimes along with the locations of the surveillance and other sensitive information. Within the Surveillance System, these videos are processed and stored on a variety of devices. The processing and storage heterogeneity of those devices, together with their network requirements, make real-time surveillance systems complex and challenging. This paper proposes a surveillance system, named as Multi-Level Video Security (MuLViS) for privacyprotected cameras. Firstly, a Smart Surveillance Security Ontology (SSSO) is integrated within the MuLViS, with the aim of autonomously selecting the privacy level matching the operating device's hardware specifications and network capabilities. Overall, along with its device-specific security, the system leads to relatively fast indexing and retrieval of surveillance video. Secondly, information within the videos are protected at the times of capturing, streaming, and storage by means of differing encryption levels. An extensive evaluation of the system, through visual inspection and statistical analysis of experimental video results, such as by the Encryption Space Ratio (ESR), has demonstrated the aptness of the security level assignments. The system is suitable for surveillance footage protection, which can be made General Data Protection Regulation (GDPR) compliant, ensuring that lawful data access respects individuals' privacy rights.

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