CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Adversarial Deep Tracking
Zhao, Fei1,2; Wang, Jinqiao1,2; Wu, Yi3,4; Tang, Ming1,2
Source PublicationIEEE Transactions on Circuits and Systems for Video Technology
ISSN1558-2205
2018
Issue1Pages:1-1
Abstract

A number of visual tracking methods achieve the state-of-the-art performance based on deep learning recently. However, most of these trackers utilize the deep neural network in regression task or classification task separately. In this paper, we propose an adversarial deep tracking framework. The framework is composed of a fully convolutional siamese neural network (regression network) and a discriminative classification network. Then, we jointly optimize the regression network and the classification network by adversarial learning. In the uniform framework, the regression network and classification network can be trained end-to-end as a whole using large amounts of video training datasets. During the testing phase, the regression network generates a response map which reflects the location and the size of the target within each candidate search patch, and the classification network discriminates which response map is the best in terms of the corresponding template patch and candidate search patch. In addition, we propose an attention visualization algorithm for our tracker, and it reflects the area that attracts the attention of our tracker during tracking. The experimental results on three large-scale visual tracking benchmarks (OTB- 100, TC-128, and VOT2016) demonstrate the effectiveness of the proposed tracking algorithm and show that our tracker performs comparably against the state-of-the-art trackers.

KeywordVisual Tracking Deep Learning Adversarial Training Attention
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23578
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.Department of Medicine, Indiana University School of Medicine
3.School of Information Engineering, Nanjing Audit University
4.University of Chinese Academy of Sciences
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,Wang, Jinqiao,Wu, Yi,et al. Adversarial Deep Tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology,2018(1):1-1.
APA Zhao, Fei,Wang, Jinqiao,Wu, Yi,&Tang, Ming.(2018).Adversarial Deep Tracking.IEEE Transactions on Circuits and Systems for Video Technology(1),1-1.
MLA Zhao, Fei,et al."Adversarial Deep Tracking".IEEE Transactions on Circuits and Systems for Video Technology .1(2018):1-1.
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