Adaptive Metadata Management System for Distributed Video Content Analysis

Carincotte, Cyril; Desurmont, Xavier; Bastide, A.

doi:10.1007/978-3-540-88458-3_30

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…We are currently investigating the possibility of using the presented formalism in a case study based on another project in real conditions to explore its useful features. The testing environment is provided by follow-up research based on a project that focused on the extraction of a structured knowledge from large amounts of multimedia content recorded over networks of cameras and microphones deployed in real sites such as the surveillance networks of the subways in Rome and Turin (Carincotte et al, 2008;Smrž et al, 2006). This case study is based on a task related to subway station monitoring: four cameras are installed in the station -two in the corridor and one on each platform.…”

Section: Other Applications Of Presented Formalismmentioning

confidence: 99%

Modeling artificial agents’ actions in context – a deontic cognitive event ontology

Vacura

2020

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Although there have been efforts to integrate Semantic Web technologies and artificial agents related AI research approaches, they remain relatively isolated from each other. Herein, we introduce a new ontology framework designed to support the knowledge representation of artificial agents’ actions within the context of the actions of other autonomous agents and inspired by standard cognitive architectures. The framework consists of four parts: 1) an event ontology for information pertaining to actions and events; 2) an epistemic ontology containing facts about knowledge, beliefs, perceptions and communication; 3) an ontology concerning future intentions, desires, and aversions; and, finally, 4) a deontic ontology for modeling obligations and prohibitions which limit agents’ actions. The architecture of the ontology framework is inspired by deontic cognitive event calculus as well as epistemic and deontic logic. We also describe a case study in which the proposed DCEO ontology supports autonomous vehicle navigation.

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Section: Other Applications Of Presented Formalismmentioning

confidence: 99%

Modeling artificial agents’ actions in context – a deontic cognitive event ontology

Vacura

2020

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“…These include: the extraction method from image sequences, the storage organization data structure and the relationship with the high-level event recognition and queries. C. Carincotte [1] proposed a method that meets the need for a generic, context-independent and adaptive system for storing and managing video analysis results, through the method of creating a metadata warehouse for storing the extracted middle level results from the raw image sequences. The proposed system can analyze the generated metadata over a long period, so as to discover general pattern of events/activities within the video monitored architecture.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition to this set, appropriate descriptions for the relation between camera and scene were also considered. To summarize, the research papers [1][2][3][4][5][6][7][8] have proposed various methods and structure of metadata organization, and have used them for the event recognition and also end-user queries.…”

Section: Literature Reviewmentioning

confidence: 99%

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Metadata extraction and organization for intelligent video surveillance system

Zhou

Pang

2010

2010 IEEE International Conference on Mechatronics and Automation

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The research for metadata extraction originates from the intelligent video surveillance system, which is widely used in outdoor and indoor environment for the aims of traffic monitor, security guard, and intelligent robot. Various features are extracted from the surveillance image sequences such as target detection, target tracking, object's shape and activities. However, the trend of more and more features being used and shared in video surveillance system calls for more attention to bridge the gap between specific analysis algorithms and enduser's expectation. This paper proposes a three-layer object oriented model to extract the surveillance metadata including shape, motion speed, and trajectory of the object emerging in image sequence. Meanwhile, the high-level semantic metadata including entry/exit point, object duration time is organized and stored which are provided for the further end-user queries. The paper also presents the experiment results in different indoor and outdoor surveillance scenarios. At last, a comparative analysis with another traditional method is presented.

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“…We notice that metadata, which contains object moving trajectory, object shape, object enter time, is widely used to support event recognition applications and user's query in surveillance videos [5]- [6]. The IBM S3 system [7] proposed the Smart Surveillance Engines (SSEs) to generate event metadata for fast searching.…”

Section: Introductionmentioning

confidence: 99%

Metadata extraction and correction for large-scale traffic surveillance videos

Zhao

Zhang

et al. 2014

2014 IEEE International Conference on Big Data (Big Data)

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Metadata is widely used to facilitate user defined queries and high-level event recognition applications in traffic surveillance videos. Current metadata extraction approaches rely on some computer vision algorithms, which are not accurate enough in the real world traffic scenes, and do not deal with big surveillance data efficiently. In this paper, we design a novel metadata extraction and metadata correction system. Firstly, we define the structure of metadata to determine which attribute (e.g., vehicle enter time, license plate number, vehicle type) we need to extract. Based on this structure, we employ a three-phase method to extract metadata. Secondly, we propose a graph-based metadata correction approach for compensating the accuracy of metadata extraction method. It fuses the big metadata of whole camera network, automatically detects suspicious metadata and corrects them based on the metadata spatial-temporal relationship and the image similarity. As the centralized framework may not be able to cope with the huge amount of data generated by traffic surveillance system, our system is implemented in a distributed fashion using Hadoop and HBase. Finally, the experimental results on real world traffic surveillance videos demonstrate the efficiency of our system, and also demonstrate that the metadata quality is significantly improved after metadata correction.

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Adaptive Metadata Management System for Distributed Video Content Analysis

Cited by 3 publications

References 8 publications

Modeling artificial agents’ actions in context – a deontic cognitive event ontology

Modeling artificial agents’ actions in context – a deontic cognitive event ontology

Metadata extraction and organization for intelligent video surveillance system

Metadata extraction and correction for large-scale traffic surveillance videos

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