Scalable video visual analytics

被引：13
|
作者：
Hoeferlin, Benjamin [1 ]
Hoeferlin, Markus [2 ]
Heidemann, Gunther [1 ]
Weiskopf, Daniel [2 ]
机构：
[1] Univ Osnabruck, D-49076 Osnabruck, Germany
[2] Univ Stuttgart, D-70174 Stuttgart, Germany
来源：
INFORMATION VISUALIZATION | 2015年 / 14卷 / 01期
关键词：
Visual analytics; video analysis; VAST Challenge 2009; scalability; situational awareness; FAST-FORWARD; RETRIEVAL; VISUALIZATION; FRAMEWORK; NEWS;
D O I：
10.1177/1473871613488571
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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.
引用
收藏
页码：10 / 26
页数：17
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