A Framework for Ontology Enriched Semantic Annotation of CCTV Video

Vrusias, Bogdan; Makris, Dimitrios; Renno, J.; Newbold, Neil; Ahmad, Khurshid; Jones, Graeme A.

doi:10.1109/wiamis.2007.4

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…Semantic and natural language description has been discussed [16] [41] as an open area of interest in surveillance. This includes a mapping between behaviours and the semantic concepts which encapsulate them.…”

Section: Introductionmentioning

confidence: 99%

Performing Content-Based Retrieval of Humans Using Gait Biometrics

Samangooei

Nixon

2008

Semantic Multimedia

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Abstract. In order to analyse surveillance video, we need to efficiently explore large datasets containing videos of walking humans. At survei llance-image resolution, the human walk (their gait) can be determined automatically, and more readily than other features such as the face. Effective analysis of such data relies on retrieval of video data which has been enriched using semantic annotations. A manual annotation process is time-consuming and prone to error due to subject bias. We explore the content-based retrieval of videos containing walking subjects, using semantic queries. We evaluate current biometric research using gait, unique in its effectiveness at recognising people at a distance. We introduce a set of semantic traits discernible by humans at a distance, outlining their psychological validity. Working under the premise that similarity of the chosen gait signature implies similarity of certain semantic traits we perform a set of semantic retrieval experiments using popular latent semantic analysis techniques from the information retrieval community.

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Section: Introductionmentioning

confidence: 99%

Performing Content-Based Retrieval of Humans Using Gait Biometrics

Samangooei

Nixon

2008

Semantic Multimedia

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“…However, VERL is rather complex and verbose, so that exhaustive definition of recognition rules is not practical for large sets without domain-specific customizations and/or user-friendly tools. Automated analysis of surveillance video is one of the most frequent applications of ontology-based activity recognition and annotation [6][7][8][9]. Chen and Nugent [10] proposed an ontology-based approach which is more similar to the one described here: an Activities of Daily Living (ADL) DL ontology was produced for activity modeling and reasoning in the context of smart homes.…”

Section: Related Workmentioning

confidence: 99%

Body Posture Recognition as a Discovery Problem: A Semantic-Based Framework

Ruta

Scioscia

Summa

et al. 2014

Active Media Technology

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Abstract. The automatic detection of human activities requires large computational resources to increase recognition performances and sophisticated capturing devices to produce accurate results. Anyway, often innovative analysis methods applied to data extracted by off-the-shelf detection peripherals can return acceptable outcomes. In this paper a framework is proposed for automated posture recognition, exploiting depth data provided by a commercial tracking device. The detection problem is handled as a semantic-based resource discovery. A simple yet general data model and a corresponding ontology create the needed terminological substratum for an automatic posture annotation via standard Semantic Web languages. Hence, a logic-based matchmaking allows to compare retrieved annotations with standard posture descriptions stored as individuals in a proper Knowledge Base. Finally, non-standard inferences and a similarity-based ranking support the discovery of the best matching posture. This framework has been implemented in a prototypical tool and preliminary experimental tests have been carried out w.r.t. a reference dataset.

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“…However, there are some approaches that use ontologies without a detailed definition of events and objects. For instance, the annotation of high-level video-surveillance concepts is proposed in [17]. The second problem, also known as knowledge-based analysis, has been similarly approached by the video research community.…”

Section: Related Workmentioning

confidence: 99%

Dynamic video surveillance systems guided by domain ontologies

SanMiguel¹,

Martínez²

2009

3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009)

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: AbstractIn this paper we describe how the knowledge related to a specific domain and the available visual analysis tools can be used to create dynamic visual analysis systems for video surveillance. Firstly, the knowledge is described in terms of application domain (types of objects, events... that can appear in such domain) and system capabilities (algorithms, detection procedures...) by using an existing ontology. Secondly, the ontology is integrated into a framework to create the visual analysis systems for each domain by inspecting the relations between the entities defined in the domain and system knowledge. Additionally, when necessary, analysis tools could be added or removed on-line. Experiments/Application of the framework show that the proposed approach for creating dynamic visual analysis systems is suitable for analyzing different video surveillance domains without decreasing the overall performance in terms of computational time or detection accuracy.

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A Framework for Ontology Enriched Semantic Annotation of CCTV Video

Abstract: Abstract

Cited by 11 publications

References 15 publications

Performing Content-Based Retrieval of Humans Using Gait Biometrics

Performing Content-Based Retrieval of Humans Using Gait Biometrics

Body Posture Recognition as a Discovery Problem: A Semantic-Based Framework

Dynamic video surveillance systems guided by domain ontologies

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