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
ISSN1051-8215
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
DOI10.1109/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
被引频次:18[WOS]   [WOS记录]     [WOS相关记录]
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.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
TCSVT_Adversarial De(6455KB)期刊论文作者接受稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Zhao, Fei]的文章
[Wang, Jinqiao]的文章
[Wu, Yi]的文章
百度学术
百度学术中相似的文章
[Zhao, Fei]的文章
[Wang, Jinqiao]的文章
[Wu, Yi]的文章
必应学术
必应学术中相似的文章
[Zhao, Fei]的文章
[Wang, Jinqiao]的文章
[Wu, Yi]的文章
相关权益政策
暂无数据
收藏/分享
文件名: TCSVT_Adversarial Deep Tracking.pdf
格式: Adobe PDF
此文件暂不支持浏览
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。