CASIA OpenIR  > 多媒体计算与图形学团队
SMART: Joint Sampling and Regression for Visual Tracking
Gao, Junyu1,2,3; Zhang, Tianzhu1,2,3; Xu, Changsheng1,2,3
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2019-08-01
Volume28Issue:8Pages:3923-3935
Corresponding AuthorXu, Changsheng(csxu@nlpr.ia.ac.cn)
AbstractMost 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.
KeywordVisual tracking deep learning sampling and regression
DOI10.1109/TIP.2019.2904434
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61432019] ; National Natural Science Foundation of China[61572498] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[61728210] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61751211] ; National Natural Science Foundation of China[61772244] ; National Natural Science Foundation of China[61472379] ; National Natural Science Foundation of China[61572296] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[U1705262] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; Beijing Natural Science Foundation[4172062]
Funding OrganizationNational Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; Beijing Natural Science Foundation
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000472609200005
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26022
Collection多媒体计算与图形学团队
Corresponding AuthorXu, Changsheng
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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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