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Antidecay LSTM for Siamese Tracking With Adversarial Learning

文献类型：期刊论文

作者Zhao, Fei4,5; Zhang, Ting6; Wu, Yi1; Tang, Ming5,7; Wang, Jinqiao2,3,5
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2021-10-01
卷号32期号:10页码:4475-4489
ISSN号2162-237X
关键词Target tracking Feature extraction Computer architecture Visualization Training Task analysis Adversarial learning deep learning long short-term memory (LSTM) visual tracking
DOI10.1109/TNNLS.2020.3018025
通讯作者Wang, Jinqiao(jqwang@nlpr.ia.ac.cn)
英文摘要Visual tracking is one of the fundamental tasks in computer vision with many challenges, and it is mainly due to the changes in the target's appearance in temporal and spatial domains. Recently, numerous trackers model the appearance of the targets in the spatial domain well by utilizing deep convolutional features. However, most of these CNN-based trackers only take the appearance variations between two consecutive frames in a video sequence into consideration. Besides, some trackers model the appearance of the targets in the long term by applying RNN, but the decay of the target's features degrades the tracking performance. In this article, we propose the antidecay long short-term memory (AD-LSTM) for the Siamese tracking. Especially, we extend the architecture of the standard LSTM in two aspects for the visual tracking task. First, we replace all of the fully connected layers with convolutional layers to extract the features with spatial structure. Second, we improve the architecture of the cell unit. In this way, the information of the target appearance can flow through the AD-LSTM without decay as long as possible in the temporal domain. Meanwhile, since there is no ground truth for the feature maps generated by the AD-LSTM, we propose an adversarial learning algorithm to optimize the AD-LSTM. With the help of adversarial learning, the Siamese network can generate the response maps more accurately, and the AD-LSTM can generate the feature maps of the target more robustly. The experimental results show that our tracker performs favorably against the state-of-the-art trackers on six challenging benchmarks: OTB-100, TC-128, VOT2016, VOT2017, GOT-10k, and TrackingNet.
WOS关键词OBJECT TRACKING ; NETWORKS
资助项目Research and Development Projects in the Key Areas of Guangdong Province[2020B010165001] ; National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61976210] ; National Natural Science Foundation of China[61806200] ; National Natural Science Foundation of China[61702510] ; National Natural Science Foundation of China[61876086]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000704111000019
资助机构Research and Development Projects in the Key Areas of Guangdong Province ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/46200]  
专题自动化研究所_模式识别国家重点实验室_图像与视频分析团队
通讯作者Wang, Jinqiao
作者单位1.Wormpex AI Res, Bellevue, WA 98004 USA
2.ObjectEye Inc, Beijing 100080, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
4.Alibaba Grp, Hangzhou 311100, Peoples R China
5.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
6.Ctr China Natl Elect Import & Export Corp CEIEC, Ctr Res & Dev, Beijing 100036, Peoples R China
7.Shenzhen Infinova Ltd, Shenzhen 518110, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Fei,Zhang, Ting,Wu, Yi,et al. Antidecay LSTM for Siamese Tracking With Adversarial Learning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021,32(10):4475-4489.
APA Zhao, Fei,Zhang, Ting,Wu, Yi,Tang, Ming,&Wang, Jinqiao.(2021).Antidecay LSTM for Siamese Tracking With Adversarial Learning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,32(10),4475-4489.
MLA Zhao, Fei,et al."Antidecay LSTM for Siamese Tracking With Adversarial Learning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 32.10(2021):4475-4489.

入库方式： OAI收割

来源：自动化研究所

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