Dynamic Collaborative Tracking

doi:10.1109/TNNLS.2018.2861838

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 图像与视频分析团队

	Dynamic Collaborative Tracking
	Zhu, Guibo1 ; Zhang, Zhaoxiang1,2,3 ; Wang, Jinqiao3,4 ; Wu, Yi 5,6; Lu, Hanqing3,4
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2019-10-01
卷号	30 期号:10 页码:3035-3046
关键词	Correlation filter (CF) distracter suppression online learning visual tracking
ISSN号	2162-237X
DOI	10.1109/TNNLS.2018.2861838
通讯作者	Zhang, Zhaoxiang(zhaoxiang.zhang@ia.ac.cn)
英文摘要	Correlation 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.
资助项目	National 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
WOS关键词	ROBUST VISUAL TRACKING ; ONLINE OBJECT TRACKING ; SPARSE-REPRESENTATION ; IMAGE CLASSIFICATION ; LOW-RANK ; OCCLUSION ; FEATURES ; FILTER
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000487199000013
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Microsoft Collaborative Research Project
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/22068]
专题	自动化研究所_模式识别国家重点实验室_图像与视频分析团队
通讯作者	Zhang, Zhaoxiang
作者单位	1.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
推荐引用方式 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.