A video interpretation platform applied to bank agency monitoring

Georis, B.; Mazière, Magali; Brémond, François; Thonnat, Monique

doi:10.1049/ic:20040097

Cited by 31 publications

(16 citation statements)

References 6 publications

(3 reference statements)

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“…3. A bank agency monitoring system has been installed and validated in four bank agencies around Paris (Georis, Mazière, Brémond, & Thonnat, 2004;see Figures 10B and 10C). 4.…”

Section: Discussionmentioning

confidence: 99%

Video-understanding framework for automatic behavior recognition

Brémond

Thonnat

Zúñiga

2006

Behavior Research Methods

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“…3. A bank agency monitoring system has been installed and validated in four bank agencies around Paris (Georis, Mazière, Brémond, & Thonnat, 2004;see Figures 10B and 10C). 4.…”

Section: Discussionmentioning

confidence: 99%

Video-understanding framework for automatic behavior recognition

Brémond

Thonnat

Zúñiga

2006

Behavior Research Methods

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“…Besides many common knowledge bases, many expert knowledge bases exist such as in the field of geography [46], medicine [38], multimedia [30], video surveillance [12], bank attacks [13] and soccer [2]. Expert knowledge bases are domain-specific, because disambiguation, jargon and structure of concepts and relations is unfeasible in open domain.…”

Section: Expert Knowledge Basesmentioning

confidence: 99%

Knowledge based query expansion in complex multimedia event detection

Boer

Schutte

Kraaij

2015

Multimed Tools Appl

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A common approach in content based video information retrieval is to perform automatic shot annotation with semantic labels using pre-trained classifiers. The visual vocabulary of state-of-the-art automatic annotation systems is limited to a few thousand concepts, which creates a semantic gap between the semantic labels and the natural language query. One of the methods to bridge this semantic gap is to expand the original user query using knowledge bases. Both common knowledge bases such as Wikipedia and expert knowledge bases such as a manually created ontology can be used to bridge the semantic gap. Expert knowledge bases have highest performance, but are only available in closed domains. Only in closed domains all necessary information, including structure and disambiguation, can be made available in a knowledge base. Common knowledge bases are often used in open domain, because it covers a lot of general information. In this research, query expansion using common knowledge bases ConceptNet and Wikipedia is compared to an expert description of the topic applied to content-based information retrieval of complex events. We run experiments on the Test Set of TRECVID MED 2014. Results show that 1) Query Expansion can improve performance compared to using no query expansion in the case that the main noun of the query could not be matched to a concept detector; 2) Query expansion using expert knowledge is not necessarily better than query expansion using common knowledge; 3) ConceptNet performs slightly better than Wikipedia; 4) Late fusion can slightly improve performance. To conclude, query expansion has potential in complex event detection.

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“…For example, Chen [26] proposed an ontology for analyzing social interaction in nursing homes; Hakeem [27] used ontolgoies for the classification of meeting videos, and Georis [28] for activities in a bank monitoring setting. To consolidate these efforts and to build a common knowledge base of domain ontologies, a collaborative initiative has been made to define ontologies for six domains of video surveillance.…”

Section: Ontology-based Activity Recognitionmentioning

confidence: 99%

Activity Recognition: Approaches, Practices and Trends

Chen

Khalil

2011

Atlantis Ambient and Pervasive Intelligence

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Activity recognition has attracted increasing attention as a number of related research areas such as pervasive computing, intelligent environments and robotics converge on this critical issue. It is also driven by growing real-world application needs in such areas as ambient assisted living and security surveillance. This chapter aims to provide an overview on existing approaches, current practices and future trends on activity recognition. It is intended to provide the necessary material to inform relevant research communities of the latest developments in this field in addition to providing a reference for researchers and system developers who are working towards the design and development of activity-based context aware applications. The chapter first reviews the existing approaches and algorithms that have been used for activity recognition in a number of related areas. It then describes the practice and lifecycle of the ontology-based approach to activity recognition that has recently been under vigorous investigation. Finally the chapter presents emerging research on activity recognition by outlining various issues and directions the field will take. IntroductionWith the advance and prevalence of low-cost low-power sensors, computing devices and wireless communication networks, pervasive computing has evolved from a vision [1] to an achievable and deployable computing paradigm. As a result, research is now being conducted in all areas related to pervasive computing, ranging from low-level data collection, to intermediate-level information processing, to high-level applications and service delivery. It is becoming increasingly evident that intelligent environments which can support both living and work places through flexible multimodal interactions, proactive service provision, and context aware personalized activity assistance will be commonplace in the very near future. For example, Smart Homes (SH) -augmented real or simulated home settings equipped with sensors, actuators and information processing systems, have been extensively studied. Work in this area has produced a number of lab-based or real-world SH prototypes [2]. Within a SH the Activities of Daily Living (ADL) of its inhabitants, usually 1

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A video interpretation platform applied to bank agency monitoring

Cited by 31 publications

References 6 publications

Video-understanding framework for automatic behavior recognition

Video-understanding framework for automatic behavior recognition

Knowledge based query expansion in complex multimedia event detection

Activity Recognition: Approaches, Practices and Trends

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