Antidecay LSTM for Siamese Tracking With Adversarial Learning

doi:10.1109/TNNLS.2020.3018025

	Antidecay LSTM for Siamese Tracking With Adversarial Learning
	Zhao, Fei1,2 ; Zhang, Ting3 ; Wu, Yi 4; Tang, Ming2,5 ; Wang, Jinqiao2,6,7
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2021-10-01
卷号	32 期号:10 页码:4475-4489
通讯作者	Wang, Jinqiao(jqwang@nlpr.ia.ac.cn)
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.
关键词	Target tracking Feature extraction Computer architecture Visualization Training Task analysis Adversarial learning deep learning long short-term memory (LSTM) visual tracking
DOI	10.1109/TNNLS.2020.3018025
关键词[WOS]	OBJECT TRACKING ; NETWORKS
收录类别	SCI
语种	英语
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]
项目资助者	Research and Development Projects in the Key Areas of Guangdong Province ; National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000704111000019
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/173211/46201
专题	紫东太初大模型研究中心_图像与视频分析
通讯作者	Wang, Jinqiao
作者单位	1.Alibaba Grp, Hangzhou 311100, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 3.Ctr China Natl Elect Import & Export Corp CEIEC, Ctr Res & Dev, Beijing 100036, Peoples R China 4.Wormpex AI Res, Bellevue, WA 98004 USA 5.Shenzhen Infinova Ltd, Shenzhen 518110, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 7.ObjectEye Inc, Beijing 100080, 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,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.