A distributed database server for continuous media

Aref, Walid G.; Catlin, Ann Christine; Elmagarmid, Ahmed K.; JinPing, Fan; Guo, Junhong; Hammad, Moustafa A.; Ilyas, Ihab F.; Marzouk, Mirette; Prabhakar, Sunil; Rezgui, Abdelmounaam; Teoh, Soon Kay; Terzi, Evimaria; Tu, Yi-Cheng; Vakali, Athena; Zhu, Xingquan

doi:10.1109/icde.2002.994764

Cited by 6 publications

(4 citation statements)

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“…The VR segments video streams into manageable file sizes using a time-based video segmentation approach (e.g., as in [4,13]), and stores them for later retrieval. The VP Suite is actually a collection of separate components, all of which extract video metadata from video streams or retrieved video segments.…”

Section: Veaw Architecturementioning

confidence: 99%

Event-driven Video Awareness Providing Physical Security

Georgakopoulos¹,

Baker²,

Nodine³

et al. 2007

World Wide Web

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The Video Event Awareness Workbench (VEAW) analyzes surveillance video from thousands of video cameras and automatically detects complex events in near-realtime-at pace with their input video streams. For events of interest to security personnel, VEAW generates and routes alerts and related video evidence to subscribing security personnel. Complex event processing in VEAW is driven by user-authored awareness specifications comprised of inter-connected spatio-temporal stream and statistical operators that consume and produce events described in VEAW's surveillance ontology. In this paper we introduce VEAW's event driven architecture and describe its solutions for automating video surveillance, including the orchestration of continuous and tasked video analysis algorithms (e.g., for entity tracking and identification), fusion of events from multiple sources in an installation-specific "world" model, and proactive information gathering to deal with missing or incomplete information (this is done by tasking video analysis algorithms and security personnel to provide it). We also discuss how VEAW deals with late arriving information (due to out-of-band video analysis tasks and overhead), as well as a related resource optimization aimed at minimizing computation costs. We illustrate the benefits of VEAW by illustrating its application on the automation of real-world security policies.

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Section: Veaw Architecturementioning

confidence: 99%

Event-driven Video Awareness Providing Physical Security

Georgakopoulos¹,

Baker²,

Nodine³

et al. 2007

World Wide Web

View full text Add to dashboard Cite

show abstract

“…The aggregation of all objects (or classes) will finally determine the descriptors and their instances. In our demonstration system [41], the expert from medical school is invited to help us in constructing the ontology descriptors and their instances, as shown in Tables 1-4. In the case that the domain experts are not available, we may utilize the information from Internet, for example, Yahoo has a hierarchy of 1,500,000 categories which might be very helpful in creating an ontology.…”

Section: Video Content Description Ontologymentioning

confidence: 99%

Hierarchical video content description and summarization using unified semantic and visual similarity

et al. 2003

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Video is increasingly the medium of choice for a variety of communication channels, resulting primarily from increased levels of networked multimedia systems. One way to keep our heads above the video sea is to provide summaries in a more tractable format. Many existing approaches are limited to exploring important low-level feature related units for summarization. Unfortunately, the semantics, content and structure of the video do not correspond to low-level features directly, even with closed-captions, scene detection, and audio signal processing. The drawbacks of existing methods are the following:(1) instead of unfolding semantics and structures within the video, low-level units usually address only the details, and (2) any important unit selection strategy based on low-level features cannot be applied to general videos. Providing users with an overview of the video content at various levels of summarization is essential for more efficient database retrieval and browsing. In this paper, we present a hierarchical video content description and summarization strategy supported by a novel joint semantic and visual similarity strategy. To describe the video content efficiently and accurately, a video content description ontology is adopted. Various video processing techniques are then utilized to construct a semi-automatic video annotation framework. By integrating acquired content description data, a hierarchical video content structure is constructed with group merging and clustering. Finally, a four layer video summary with different granularities is assembled to assist users in unfolding the video content in a progressive way. Experiments on real-word videos have validated the effectiveness of the proposed approach.

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“…These VDBMS system components are described in this paper, and details can be found in the literature [2,11,14,18]. We present VDBMS as a research platform because it provides an open and flexible environment for investigating new research areas related to video database management, including the implementation, integration, and evaluation of new and existing algorithms.…”

Section: Introductionmentioning

confidence: 98%

VDBMS: A testbed facility for research in video database benchmarking

et al. 2004

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Real-world video-based applications require database technology that is capable of storing digital video in the form of video databases and providing content-based video search and retrieval. Methods for handling traditional data storage, query, search, retrieval, and presentation cannot be extended to provide this functionality. The VDBMS research initiative is motivated by the requirements of videobased applications to search and retrieve portions of video data based on content and by the need for testbed facilities to facilitate research in the area of video database management. In this paper we describe the VDBMS video database research platform, a system that supports comprehensive and efficient database management for digital video. Our fundamental concept is to provide a full range of functionality for video as a well-defined abstract database data type, with its own description, parameters, and applicable methods. Research problems that are addressed by VDBMS to support the handling of video data include MPEG7 standard multimedia content representation, algorithms for image-based shot detection, image processing techniques for extracting lowlevel visual features, a high-dimensional indexing technique to access the high-dimensional feature vectors extracted by image preprocessing, multimedia query processing and optimization, new query operators, real-time stream management, a search-based buffer management policy, and an access control model for selective, content-based access to streaming video. VDBMS also provides an environment for testing the correctness and scope of new video processing techniques, measuring the performance of algorithms in a standardized way, and comparing the performance of different implementations of an algorithm or component. We are currently developing video component wrappers with well-defined interfaces to facilitate the modification or replacement of video processing components. The ultimate goal of the VDBMS project is a flexible, extensible framework that can be used by the research community for developing, testing, and benchmarking video database technologies.

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A distributed database server for continuous media

Cited by 6 publications

References 0 publications

Event-driven Video Awareness Providing Physical Security

Event-driven Video Awareness Providing Physical Security

Hierarchical video content description and summarization using unified semantic and visual similarity

VDBMS: A testbed facility for research in video database benchmarking

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