CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Domain Adaptation Tracker with Global and Local Searching
Zhao, Fei1,2; Zhang,Ting1,2,3; Wu, Yi4; Wang, Jinqiao1,2; Tang, Ming1,2
Source PublicationIEEE Access
ISSN2169-3536
2018
Issue6Pages:42997 - 43008
Abstract

For the convolutional neural network (CNN)-based trackers, most of them locate the target only within a local area, which makes the trackers hard to recapture the target after drifting into the background. Besides, most state-of-the-art trackers spend a large amount of time on training the CNN-based classification networks online to adapt to the current domain. In this paper, to address the two problems, we propose a robust domain adaptation tracker based on the CNNs. The proposed tracker contains three CNNs: a local location network (LL-Net), a global location network (GL-Net), and a domain adaptation classification network (DA-Net). For the former problem, if we come to the conclusion that the tracker drifts into the background based on the output of the LL-Net, we will search for the target in a global area of the current frame based on the GL-Net. For the latter problem, we propose a CNN-based DA-Net with a domain adaptation (DA) layer. By pre-training the DA-Net offline, the DA-Net can adapt to the current domain by only updating the parameters of the DA layer in one training iteration when the online training is triggered, which makes the tracker run five times faster than MDNet with comparable tracking performance. The experimental results show that our tracker performs favorably against the state-of-the-art trackers on three popular benchmarks.

KeywordConvolutional Neural Networks Domain Adaptation Online Training Visual Tracking
DOI10.1109/ACCESS.2018.2862878
Indexed BySCI
Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23577
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorWang, Jinqiao
Affiliation1.National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.R&D Center, China National Electronics Import & Export Corporation
4.Department of Medicine, Indiana University School of Medicine
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhao, Fei,Zhang,Ting,Wu, Yi,et al. Domain Adaptation Tracker with Global and Local Searching[J]. IEEE Access,2018(6):42997 - 43008.
APA Zhao, Fei,Zhang,Ting,Wu, Yi,Wang, Jinqiao,&Tang, Ming.(2018).Domain Adaptation Tracker with Global and Local Searching.IEEE Access(6),42997 - 43008.
MLA Zhao, Fei,et al."Domain Adaptation Tracker with Global and Local Searching".IEEE Access .6(2018):42997 - 43008.
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