Visual Tracking With Multiview Trajectory Prediction

doi:10.1109/TIP.2020.3014952

	Visual Tracking With Multiview Trajectory Prediction
	Minye Wu1,2,3 ; Haibin Ling 4; Ning Bi 1,3,5; Shenghua Gao3 ; Qiang Hu3 ; Hao Sheng 6; Jingyi Yu3
	2020
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING (IF:10.8[JCR-2023],12.1[5-Year])
ISSN	1941-0042
卷号	29 页码:8355-8367
发表状态	已发表
DOI	10.1109/TIP.2020.3014952
摘要	Recent progresses in visual tracking have greatly improved the tracking performance. However, challenges such as occlusion and view change remain obstacles in real world deployment. A natural solution to these challenges is to use multiple cameras with multiview inputs, though existing systems are mostly limited to specific targets (e.g. human), static cameras, and/or require camera calibration. To break through these limitations, we propose a generic multiview tracking (GMT) framework that allows camera movement, while requiring neither specific object model nor camera calibration. A key innovation in our framework is a cross-camera trajectory prediction network (TPN), which implicitly and dynamically encodes camera geometric relations, and hence addresses missing target issues such as occlusion. Moreover, during tracking, we assemble information across different cameras to dynamically update a novel collaborative correlation filter (CCF), which is shared among cameras to achieve robustness against view change. The two components are integrated into a correlation filter tracking framework, where features are trained offline using existing single view tracking datasets. For evaluation, we first contribute a new generic multiview tracking dataset (GMTD) with careful annotations, and then run experiments on the GMTD and CAMPUS datasets. The proposed GMT algorithm shows clear advantages in terms of robustness over state-of-the-art ones.
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收录类别	SCI ; EI ; SCIE
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/122662
专题	信息科学与技术学院_博士生信息科学与技术学院_PI研究组_虞晶怡组信息科学与技术学院_PI研究组_高盛华组
作者单位	1.Yoke Intelligence, Shanghai, China 2.Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China 3.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 4.Computer Science Department, Stony Brook University, Stony Brook, USA 5.School of Computing, University of Leeds, Leeds, U.K. 6.State Key Laboratory of Software Development Environment, Beijing Advanced Innovation Center for Big Data and Brain Computing, School of Computer Science and Engineering, Beihang University, Beijing, China
第一作者单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Minye Wu,Haibin Ling,Ning Bi,et al. Visual Tracking With Multiview Trajectory Prediction[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:8355-8367.
APA	Minye Wu.,Haibin Ling.,Ning Bi.,Shenghua Gao.,Qiang Hu.,...&Jingyi Yu.(2020).Visual Tracking With Multiview Trajectory Prediction.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,8355-8367.
MLA	Minye Wu,et al."Visual Tracking With Multiview Trajectory Prediction".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):8355-8367.