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Surveillance Video Synopsis via Scaling Down Objects
Li, Xuelong1; Wang, Zhigang2,3; Lu, Xiaoqiang1
作者部门光学影像学习与分析中心
2016-02-01
发表期刊IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
卷号25期号:2页码:740-755
产权排序1
摘要

Video synopsis is an effective technique to provide a compact representation of the original video by removing spatiotemporal redundancies and by preserving the essential activities. Most current approaches for video synopsis will cause collisions among objects, especially when the video is condensed much. In this paper, we present an approach for video synopsis to reduce the collisions. Our approach first shifts active objects along the time axis to compact the original video. Then, the sizes of the objects are reduced when collisions occur. Meanwhile, the geometric centroids of the objects will be kept unchanged to preserve the location information. Our contributions are threefold. First, an approach is proposed to decrease collisions in the synopsis video through reducing the sizes of the objects. Second, an optimization framework is developed to indicate the optimal time position and the appropriate reduction coefficient for each object. Finally, some metrics are proposed, and several experiments are carried out to evaluate the proposed approach. The experiments have demonstrated that the synopsis video produced by our approach has much fewer collisions while the compression ratio is high.

文章类型Article
关键词Video Synopsis Surveillance Reduce Collision Reduce Size Optimization
WOS标题词Science & Technology ; Technology
DOI10.1109/TIP.2015.2507942
收录类别SCI ; EI
关键词[WOS]GRAPH CUTS ; MODEL ; OPTIMIZATION ; ATTENTION ; IMAGES
语种英语
WOS研究方向Computer Science ; Engineering
项目资助者Fundamental Research Funds for the Central Universities(3102015BJ(II)JJZ01) ; National Basic Research Program of China (973 Program)(2012CB719905) ; National Natural Science Foundation of China(61472413) ; Key Research Program through the Chinese Academy of Sciences(KGZD-EW-T03) ; Open Research Fund of the Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences(LSIT201408)
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000383905800019
引用统计
被引频次:72[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/28247
专题光谱成像技术研究室
作者单位1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China
2.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China
3.Northwestern Polytech Univ, Ctr Opt IMagery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714
Li, Xuelong,Wang, Zhigang,Lu, Xiaoqiang. Surveillance Video Synopsis via Scaling Down Objects[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(2):740-755.
APA Li, Xuelong,Wang, Zhigang,&Lu, Xiaoqiang.(2016).Surveillance Video Synopsis via Scaling Down Objects.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(2),740-755.
MLA Li, Xuelong,et al."Surveillance Video Synopsis via Scaling Down Objects".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.2(2016):740-755.
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