| Severely Blurred Object Tracking by Learning Deep Image Representations | |
Ding, Jianwei1; Huang, Yongzhen3 ; Liu, Wei2; Huang, Kaiqi3
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| 发表期刊 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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| 2016-02-01 | |
| 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 |
| DOI | 10.1109/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 | |
| 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 |
| 推荐引用方式 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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| dingjianwei-2016-.pd（3657KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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