Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Adversarial Deep Tracking | |
Zhao, Fei1; Wang, Jinqiao1; Wu, Yi2,3; Tang, Ming1 | |
发表期刊 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY |
ISSN | 1051-8215 |
2019-07-01 | |
卷号 | 29期号:7页码:1998-2011 |
通讯作者 | Wang, Jinqiao(jqwang@nlpr.ia.ac.cn) ; Wu, Yi(ywu.china@gmail.com) |
摘要 | 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 data sets. 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. |
关键词 | Visual tracking deep learning adversarial training attention |
DOI | 10.1109/TCSVT.2018.2856540 |
关键词[WOS] | VISUAL TRACKING ; OBJECT TRACKING ; NETWORKS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Natural Science Foundation of China[61772527] ; Natural Science Foundation of China[61772527] |
项目资助者 | Natural Science Foundation of China |
WOS研究方向 | Engineering |
WOS类目 | Engineering, Electrical & Electronic |
WOS记录号 | WOS:000473623800009 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23578 |
专题 | 模式识别国家重点实验室_图像与视频分析 |
通讯作者 | Wang, Jinqiao; Wu, Yi |
作者单位 | 1.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Indiana Univ Sch Med, Dept Med, Indianapolis, IN 46202 USA 3.Nanjing Audit Univ, Sch Informat Engn, Nanjing 211815, Jiangsu, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Zhao, Fei,Wang, Jinqiao,Wu, Yi,et al. Adversarial Deep Tracking[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2019,29(7):1998-2011. |
APA | Zhao, Fei,Wang, Jinqiao,Wu, Yi,&Tang, Ming.(2019).Adversarial Deep Tracking.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,29(7),1998-2011. |
MLA | Zhao, Fei,et al."Adversarial Deep Tracking".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 29.7(2019):1998-2011. |
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