CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Dynamic Collaborative Tracking
Zhu, Guibo1; Zhang, Zhaoxiang1,2,3; Wang, Jinqiao3,4; Wu, Yi5,6; Lu, Hanqing3,4
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
2019-10-01
Volume30Issue:10Pages:3035-3046
Corresponding AuthorZhang, Zhaoxiang(zhaoxiang.zhang@ia.ac.cn)
AbstractCorrelation filter has been demonstrated remarkable success for visual tracking recently. However, most existing methods often face model drift caused by several factors, such as unlimited boundary effect, heavy occlusion, fast motion, and distracter perturbation. To address the issue, this paper proposes a unified dynamic collaborative tracking framework that can perform more flexible and robust position prediction. Specifically, the framework learns the object appearance model by jointly training the objective function with three components: target regression submodule, distracter suppression submodule, and maximum margin relation submodule. The first submodule mainly takes advantage of the circulant structure of training samples to obtain the distinguishing ability between the target and its surrounding background. The second submodule optimizes the label response of the possible distracting region close to zero for reducing the peak value of the confidence map in the distracting region. Inspired by the structure output support vector machines, the third submodule is introduced to utilize the differences between target appearance representation and distracter appearance representation in the discriminative mapping space for alleviating the disturbance of the most possible hard negative samples. In addition, a CUR filter as an assistant detector is embedded to provide effective object candidates for alleviating the model drift problem. Comprehensive experimental results show that the proposed approach achieves the state-of-the-art performance in several public benchmark data sets.
KeywordCorrelation filter (CF) distracter suppression online learning visual tracking
DOI10.1109/TNNLS.2018.2861838
WOS KeywordROBUST VISUAL TRACKING ; ONLINE OBJECT TRACKING ; SPARSE-REPRESENTATION ; IMAGE CLASSIFICATION ; LOW-RANK ; OCCLUSION ; FEATURES ; FILTER
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2018YFB1004600] ; National Natural Science Foundation of China[61702510] ; National Natural Science Foundation of China[61773375] ; National Natural Science Foundation of China[61375036] ; National Natural Science Foundation of China[61602481] ; National Natural Science Foundation of China[61370036] ; National Natural Science Foundation of China[61772277] ; National Natural Science Foundation of China[61772527] ; Microsoft Collaborative Research Project
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Microsoft Collaborative Research Project
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000487199000013
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22068
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorZhang, Zhaoxiang
Affiliation1.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 200031, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
5.Nanjing Audit Univ, Sch Technol, Nanjing 211815, Jiangsu, Peoples R China
6.Indiana Univ Sch Med, Dept Med, Indianapolis, IN 46202 USA
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
Zhu, Guibo,Zhang, Zhaoxiang,Wang, Jinqiao,et al. Dynamic Collaborative Tracking[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(10):3035-3046.
APA Zhu, Guibo,Zhang, Zhaoxiang,Wang, Jinqiao,Wu, Yi,&Lu, Hanqing.(2019).Dynamic Collaborative Tracking.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(10),3035-3046.
MLA Zhu, Guibo,et al."Dynamic Collaborative Tracking".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.10(2019):3035-3046.
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