Ontology-based forensic event detection using inference rules

Sobhani, Faranak; Izquierdo, Ebroul; Piatrik, Tomas

doi:10.1109/ice.2017.8279938

Cited by 6 publications

(9 citation statements)

References 18 publications

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“…Four phases are: visual feature extraction, nucleotide sequence formation, multi view video correlation with FASTA and event summarisation. Sobhani et al [7] develops an ontology for formally representing the major events in a surveillance system which is extended to forensics too. The ontology is built on the famous DOLCE ontology which is extensively used for linguistic and cognitive modelling of knowledge.…”

Section: Surveillance Video Summarisation Frameworkmentioning

confidence: 99%

“…The proposed framework identifies five most common classes of events as relevant to surveillance videos. The events that the proposed model is trained to identify are hitting, running, kicking, shooting and punching [7,8]. These actions can be stated as undesirable events which are frequently sought in surveillance videos.…”

Section: Stage 2: Stacked Bidirectional Gru Modelmentioning

confidence: 99%

“…The proposed model exhibited better performance by incorporating GRUs over LSTMs. The fundamental equations that govern GRUs are stated in (5)(6)(7)(8).…”

Section: Bidirectional Grusmentioning

confidence: 99%

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An aggregated deep convolutional recurrent model for event based surveillance video summarisation: A supervised approach

Sreeja

Kovoor

2021

IET Computer Vision

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Surveillance video summarisation is characterised by extracting video segments containing abnormal events from surveillance video footages. Accurate identification of abnormal events from surveillance footages is of paramount importance in surveillance video summarisation. Accordingly, the proposed framework builds an aggregated convolutional recurrent model that can precisely detect the suspicious events in a surveillance footage, by employing a supervised learning which is found to yield better results compared with unsupervised counterparts. The preliminary stage in the model is a multilayer Convolutional Neural Network for frame-level feature extraction followed by stacked bidirectional Gated Recurrent Unit for sequence-level feature extraction and classification. Since the video clips used for training are not implicit to surveillance, a block-based approach for testing on surveillance videos is proposed. The results evaluated on two custom datasets, Streets and Campus, prove that the proposed model produces remarkable results leveraging the properties of bidirectional GRU with supervised learning. Extensive experimental analysis on selection of optimum architecture is conducted which substantiates the significance of stacked bidirectional GRUs over unidirectional ones. Additionally, qualitative results ensure that summaries produced are concise, representative, complete, diverse and informative. Moreover, comparison of the performance of the proposed model with state of the art certainly proves the superiority of the proposed model.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Surveillance Video Summarisation Frameworkmentioning

confidence: 99%

Section: Stage 2: Stacked Bidirectional Gru Modelmentioning

confidence: 99%

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An aggregated deep convolutional recurrent model for event based surveillance video summarisation: A supervised approach

Sreeja

Kovoor

2021

IET Computer Vision

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“…The ontology distinguishes simple events with low or medium level semantics and complex events with a high level of semantic that may also be obtained by combining simple events. 14 From 2011 to 2014, the CAPER project, funded by the European Commission through the Seventh Framework Programme for Research and Technological Development, created a common platform for the prevention of organized crime through sharing, exploitation, and analysis of open and private information sources. 15 In the CAPER project, a conceptual structure of the cross-border organized crime has been developed by analyzing the work of EUROPOL and its databases, resulting in a Europol Organized Crime Structure (OCS), which is a supranational structure embedding the speci¯c natural structures, overcoming limitations due to nonharmonized criminal law systems.…”

Section: Identi¯cation Of Ontologiesmentioning

confidence: 99%

“…15 In the CAPER project, a conceptual structure of the cross-border organized crime has been developed by analyzing the work of EUROPOL and its databases, resulting in a Europol Organized Crime Structure (OCS), which is a supranational structure embedding the speci¯c natural structures, overcoming limitations due to nonharmonized criminal law systems. 14 Two ontologies have been developed in CAPER. They are not connected since they address di®erent problems: interoperability in information exchange on the one side and analytics on the other.…”

Section: Identi¯cation Of Ontologiesmentioning

confidence: 99%

Common Representational Model and Ontologies for Effective Law Enforcement Solutions

Kozik

Choraś

Pawlicki

et al. 2020

Vietnam J. Comp. Sci.

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Ontologies have developed into a prevailing technique for establishing semantic interoperability among heterogeneous systems transacting information. An ontology is an unambiguous blueprint of a concept. For Artificial Intelligence, only the defined notions can be considered existent. Thus, in relation to AI, an ontology can be understood as part of a program which delineates a collection of descriptions. An ontology, therefore, correlates the labels of the entities in the universe of discourse with wording that holds meaning for humans, explaining what those labels signify, along with the precise principles that force the interpretation and semantic utilization of these labels. An ontology constitutes a proper statement of a logical theory. It is a crucial component of a system with the capability to process, manage, analyze, correlate and reason from the large datasets characterized by heterogeneity. This paper depicts the process of development of a Common Representational Model (CRM) on top of several ontologies, taxonomies and classifications to facilitate computational and data mining functionalities. The building blocks of said CRM are delineated in detail, as well as its application in a specific use case.

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The Identification and Creation of Ontologies for the Use in Law Enforcement AI Solutions – MAGNETO Platform Use Case

Kozik

Choraś

Pawlicki

et al. 2019

Computational Collective Intelligence

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Ontology-based forensic event detection using inference rules

Cited by 6 publications

References 18 publications

An aggregated deep convolutional recurrent model for event based surveillance video summarisation: A supervised approach

An aggregated deep convolutional recurrent model for event based surveillance video summarisation: A supervised approach

Common Representational Model and Ontologies for Effective Law Enforcement Solutions

The Identification and Creation of Ontologies for the Use in Law Enforcement AI Solutions – MAGNETO Platform Use Case

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