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Severely Blurred Object Tracking by Learning Deep Image Representations
Ding, Jianwei1; Huang, Yongzhen3; Liu, Wei2; Huang, Kaiqi3
2016-02-01
发表期刊IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
卷号26期号:2页码:319-331
文章类型Article
摘要An implicit assumption in many generic object trackers is that the videos are blur free. However, motion blur is very common in real videos. The performance of a generic object tracker may drop significantly when it is applied to videos with severe motion blur. In this paper, we propose a new Tracking-Learning-Data approach to transfer a generic object tracker to a blur-invariant object tracker without deblurring image sequences. Before object tracking, a large set of unlabeled images is used to learn objects' visual prior knowledge, which is then transferred to the appearance model of a specific target. During object tracking, online training samples are collected from the tracking results and the context information. Different blur kernels are involved with the training samples to increase the robustness of the appearance model to severe blur, and the motion parameters of the object are estimated in the particle filter framework. Extensive experimental results demonstrate that the proposed algorithm can robustly track objects not only in severely blurred videos but also in other challenging scenes.
关键词Deep Learning Object Tracking Severe Blur
WOS标题词Science & Technology ; Technology
DOI10.1109/TCSVT.2015.2406231
收录类别SCI
语种英语
项目资助者Fundamental Research Funds for Central Universities(2014JKF01116) ; National High Technology Research and Development Program of China(2013AA014604) ; National Natural Science Foundation of China(61402484 ; SAMSUNG Global Research Outreach Program ; CCF-Tencent Program ; 360 OpenLab Program ; 61203252)
WOS研究方向Engineering
WOS类目Engineering, Electrical & Electronic
WOS记录号WOS:000370935900005
引用统计
被引频次:10[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/11356
专题智能感知与计算研究中心
作者单位1.Peoples Publ Secur Univ China, Beijing 430072, Peoples R China
2.Nanyang Normal Univ, Nanyang 450001, Henan, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
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GB/T 7714
Ding, Jianwei,Huang, Yongzhen,Liu, Wei,et al. Severely Blurred Object Tracking by Learning Deep Image Representations[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2016,26(2):319-331.
APA Ding, Jianwei,Huang, Yongzhen,Liu, Wei,&Huang, Kaiqi.(2016).Severely Blurred Object Tracking by Learning Deep Image Representations.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,26(2),319-331.
MLA Ding, Jianwei,et al."Severely Blurred Object Tracking by Learning Deep Image Representations".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 26.2(2016):319-331.
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