Scalable video visual analytics

Autor(en): Hoeferlin, Benjamin
Hoeferlin, Markus
Heidemann, Gunther 
Weiskopf, Daniel
Stichwörter: Computer Science; Computer Science, Software Engineering; FAST-FORWARD; FRAMEWORK; NEWS; RETRIEVAL; scalability; situational awareness; VAST Challenge 2009; video analysis; Visual analytics; VISUALIZATION
Erscheinungsdatum: 2015
Herausgeber: SAGE PUBLICATIONS LTD
Journal: INFORMATION VISUALIZATION
Volumen: 14
Ausgabe: 1, SI
Startseite: 10
Seitenende: 26
Zusammenfassung: 
Video visual analytics is the research field that addresses scalable and reliable analysis of video data. The vast amount of video data in typical analysis tasks renders manual analysis by watching the video data impractical. However, automatic evaluation of video material is not reliable enough, especially when it comes to semantic abstraction from the video signal. In this article, we describe the video visual analytics method that combines the complementary strengths of human recognition and machine processing. After inspecting the challenges of scalable video analysis, we derive the main components of visual analytics for video data. Based on these components, we present our video visual analytics system that has its origins in our IEEE VAST Challenge 2009 participation.
ISSN: 14738716
DOI: 10.1177/1473871613488571

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