Unsupervised, efficient and scalable key-frame selection for automatic summarization of surveillance videos
Unsupervised, efficient and scalable key-frame selection for automatic summarization of surveillance videos
复制标题
无监督、高效且可扩展的关键帧选择,用于自动总结监控视频
DOI:
10.1007/s11042-016-3263-z
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发表时间:
2017-03
影响因子:
3.6
通讯作者:
Yan Peng
中科院分区:
文献类型:
--
作者:
Lu Guoliang;Zhou Yiqi;Li Xueyong;Yan Peng
Recent years have witnessed a dramatical growth of the deployment of vision-based surveillance in public spaces. Automatic summarization of surveillance videos (ASOSV) is hence becoming more and more desirable in many real-world applications. For this purpose, a novel frame-selection framework is proposed in the present paper, which has three properties: 1)un-supervision:it can work without requirements of any supervised learning or training; 2)efficiency:it can work very fast, with experiments demonstrating efficiency faster than real-timeness and 3)scalability:it can achieve a hierarchical analysis/overview of video content. The performance of proposed framework is systematically evaluated and compared with various state-of-the-art frame selection techniques on some collected video sequences and publicly-availableViSORdataset. The experimental results demonstrate promising performance and good applicability for real-world problems.
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DOI:
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发表时间:
2005-12
期刊:
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DOI:
10.1016/b978-012369387-7/50009-5
发表时间:
2006
期刊:
--
影响因子:
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作者:
Z. Xiong;R. Radhakrishnan;Ajay Divakaran;Y. Rui;Thomas S. Huang
通讯作者:
Z. Xiong;R. Radhakrishnan;Ajay Divakaran;Y. Rui;Thomas S. Huang