Visual Object Tracking Based on Deep Neural Network

doi:10.1155/2022/2154463

	Visual Object Tracking Based on Deep Neural Network
	Diao, Zhifeng 1; Sun, Fanglei2
	2022
发表期刊	MATHEMATICAL PROBLEMS IN ENGINEERING (IF:1.430[JCR-2021],1.393[5-Year])
ISSN	1024-123X
EISSN	1563-5147
卷号	2022
发表状态	已发表
DOI	10.1155/2022/2154463
摘要	Computer vision systems cannot function without visual target tracking. Intelligent video monitoring, medical treatment, human-computer interaction, and traffic management all stand to benefit greatly from this technology. Although many new algorithms and methods emerge every year, the reality is complex. Targets are often disturbed by factors such as occlusion, illumination changes, deformation, and rapid motion. Solving these problems has also become the main task of visual target tracking researchers. As with the development for deep neural networks and attention mechanisms, object-tracking methods with deep learning show great research potential. This paper analyzes the abovementioned difficult factors, uses the tracking framework based on deep learning, and combines the attention mechanism model to accurately model the target, aiming to improve tracking algorithm. In this work, twin network tracking strategy with dual self-attention is designed. A dual self-attention mechanism is used to enhance feature representation of the target from the standpoint of space and channel, with the goal of addressing target deformation and other problems. In addition, adaptive weights and residual connections are used to enable adaptive attention feature selection. A Siamese tracking network is used in conjunction with the proposed dual self-attention technique. Massive experimental results show our proposed method improves tracking performance, and tracking strategy achieves an excellent tracking effect. © 2022 Zhifeng Diao and Fanglei Sun.
关键词	Clutter (information theory) Attention mechanisms Deep neural networks Computer vision system Human computer interaction Intelligent video Target tracking Interaction management Medical treatment Tracking strategies Traffic management Video monitoring Visual object tracking Visual target tracking
URL	查看原文
收录类别	SCI ; EI ; SCIE
语种	英语
WOS研究方向	Engineering ; Mathematics
WOS类目	Engineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications
WOS记录号	WOS:000859381800004
出版者	Hindawi Limited
EI入藏号	20223112473402
EI主题词	Deformation
EI分类号	461.4 Ergonomics and Human Factors Engineering；716.1 Information Theory and Signal Processing
原始文献类型	Journal article (JA)
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/211738
专题	创意与艺术学院_特聘教授组_汪军组
通讯作者	Sun, Fanglei
作者单位	1.Tongji Univ, Coll Design & Innovat, Shanghai, Peoples R China 2.ShanghaiTech Univ, Sch Creat & Art, Shanghai, Peoples R China
通讯作者单位	创意与艺术学院
推荐引用方式 GB/T 7714	Diao, Zhifeng,Sun, Fanglei. Visual Object Tracking Based on Deep Neural Network[J]. MATHEMATICAL PROBLEMS IN ENGINEERING,2022,2022.
APA	Diao, Zhifeng,&Sun, Fanglei.(2022).Visual Object Tracking Based on Deep Neural Network.MATHEMATICAL PROBLEMS IN ENGINEERING,2022.
MLA	Diao, Zhifeng,et al."Visual Object Tracking Based on Deep Neural Network".MATHEMATICAL PROBLEMS IN ENGINEERING 2022(2022).