Knowledge Commons of Institute of Automation,CAS
SMART: Joint Sampling and Regression for Visual Tracking | |
Gao, Junyu1,2,3![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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ISSN | 1057-7149 |
2019-08-01 | |
卷号 | 28期号:8页码:3923-3935 |
摘要 | Most existing trackers are either sampling-based or regression-based methods. Sampling-based methods estimate the target state by sampling many target candidates. Although these methods achieve significant performance, they often suffer from a high computational burden. Regression-based methods often learn a computationally efficient regression function to directly predict the geometric distortion between frames. However, most of these methods require large-scale external training videos and are still not very impressive in terms of accuracy. To make both types of methods enhance and complement each other, in this paper, we propose a joint sampling and regression scheme for visual tracking, which leverages the region proposal network by a novel design. Specifically, our method can jointly exploit discriminative target proposal generation and structural target regression to predict target location in a simple feedforward propagation. We evaluate the proposed method on five challenging benchmarks, and extensive experimental results demonstrate that our method performs favorably compared with state-of-the-art trackers with respect to both accuracy and speed. |
关键词 | Visual tracking deep learning sampling and regression |
DOI | 10.1109/TIP.2019.2904434 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61751211] ; National Natural Science Foundation of China[61728210] ; National Natural Science Foundation of China[61772244] ; Beijing Natural Science Foundation[4172062] ; National Natural Science Foundation of China[61572296] ; National Natural Science Foundation of China[61472379] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[61572498] ; National Natural Science Foundation of China[61721004] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[61432019] ; National Natural Science Foundation of China[61432019] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[U1705262] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61572498] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[61472379] ; National Natural Science Foundation of China[61572296] ; Beijing Natural Science Foundation[4172062] ; National Natural Science Foundation of China[61772244] ; National Natural Science Foundation of China[61728210] ; National Natural Science Foundation of China[61751211] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000472609200005 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/26022 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
通讯作者 | Xu, Changsheng |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 518055, Peoples R China 3.Peng Cheng Lab, Shenzhen 518055, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Gao, Junyu,Zhang, Tianzhu,Xu, Changsheng. SMART: Joint Sampling and Regression for Visual Tracking[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(8):3923-3935. |
APA | Gao, Junyu,Zhang, Tianzhu,&Xu, Changsheng.(2019).SMART: Joint Sampling and Regression for Visual Tracking.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(8),3923-3935. |
MLA | Gao, Junyu,et al."SMART: Joint Sampling and Regression for Visual Tracking".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.8(2019):3923-3935. |
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