Knowledge Commons of Institute of Automation,CAS
Manipulating Template Pixels for Model Adaptation of Siamese Visual Tracking | |
Li, Zhenbang1,2![]() ![]() ![]() ![]() | |
发表期刊 | IEEE SIGNAL PROCESSING LETTERS
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ISSN | 1070-9908 |
2020 | |
卷号 | 27页码:1690-1694 |
摘要 | In this letter, we show that the challenging model adaptation task in visual object tracking can be handled by simply manipulating pixels of the template image in Siamese networks. For a target that is not included in the offline training set, a slight modification of the template image pixels will improve the prediction result of the offline trained Siamese network. The popular adversarial example generation methods can be used to perform template pixel manipulation for model adaptation. Different from current template update methods, which aim to combine the target features from previous frames, we focus on the initial adaptation using target ground-truth in the first frame. Our model adaptation method is pluggable, in the sense that it does not alter the overall architecture of its base tracker. To our knowledge, this work is the first attempt to directly manipulating template pixels for model adaptation in Siamese-based trackers. Extensive experiments on recent benchmarks demonstrate that our method achieves better performance than some other state-of-the-art trackers. Our code is available at https://github.com/lizhenbang56/MTP. |
关键词 | Adaptation models Target tracking Task analysis Visualization Feature extraction Object tracking Training Model adaptation siamese networks visual tracking |
DOI | 10.1109/LSP.2020.3025406 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key R&D Program of China[2018AAA0102802] ; National Key R&D Program of China[2018AAA0102803] ; National Key R&D Program of China[2018AAA0102800] ; NSFC-General Technology Collaborative Fund for Basic Research[U1636218] ; Natural Science Foundation of China[61751212] ; Natural Science Foundation of China[61721004] ; Natural Science Foundation of China[61772225] ; Natural Science Foundation of China[61972394] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC040] ; National Natural Science Foundation of Guangdong[2018B030311046] |
项目资助者 | National Key R&D Program of China ; NSFC-General Technology Collaborative Fund for Basic Research ; Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; National Natural Science Foundation of Guangdong |
WOS研究方向 | Engineering |
WOS类目 | Engineering, Electrical & Electronic |
WOS记录号 | WOS:000576408300008 |
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 |
国重实验室规划方向分类 | 实体人工智能系统感认知 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42088 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
通讯作者 | Gao, Jin |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 3.Beijing Inst Basic Med Sci, Brain Sci Ctr, Beijing 100850, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, 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 | Li, Zhenbang,Li, Bing,Gao, Jin,et al. Manipulating Template Pixels for Model Adaptation of Siamese Visual Tracking[J]. IEEE SIGNAL PROCESSING LETTERS,2020,27:1690-1694. |
APA | Li, Zhenbang,Li, Bing,Gao, Jin,Li, Liang,&Hu, Weiming.(2020).Manipulating Template Pixels for Model Adaptation of Siamese Visual Tracking.IEEE SIGNAL PROCESSING LETTERS,27,1690-1694. |
MLA | Li, Zhenbang,et al."Manipulating Template Pixels for Model Adaptation of Siamese Visual Tracking".IEEE SIGNAL PROCESSING LETTERS 27(2020):1690-1694. |
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