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Target tracking based on neural network depth feature and texture fusion
Cao YZ(曹永战)1; Liu, Meiju1; Yang SK(杨尚奎)2; Yang, Guodong3; Chen P(陈鹏)2; Zhu SY(朱树云)2; Ge Z(葛壮)2; Liu YW(刘玉旺)2
Department空间自动化技术研究室
Conference Name2019 5th International Conference on Energy Equipment Science and Engineering, ICEESE 2019
Conference DateNovember 29 - December 1, 2019
Conference PlaceHarbin, China
Source Publication2019 5th International Conference on Energy Equipment Science and Engineering
PublisherIOP
Publication PlaceBristol, UK
2019
Pages1-6
Indexed ByEI
EI Accession number20201908616415
Contribution Rank2
ISSN1755-1307
AbstractThis paper presents a method of target tracking based on convolution neural network and texture feature fusion. The lower layer of the convolutional neural network can extract some spatial structure, shape and other features of the target. High-level level can extract relatively abstract semantic information. In this paper, vgg-m convolutional neural network is adopted to realize tracking by adaptive fusion of the extracted depth features of Conv2 and Conv5 with the texture features extracted by two-dimensional Gabor filtering. In this paper, the experimental analysis of this method is carried out on the OTB2013 data set, and the results show that this method can achieve more accurate positioning of the target and has a strong timeliness.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/26754
Collection空间自动化技术研究室
Corresponding AuthorLiu YW(刘玉旺)
Affiliation1.Information and Control Engineering Faculty, Shenyang Jianzhu University, Shenyang, LIAONING 110168, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.School of Mechanical Engineering and Automation, Northeastern University 3 Wenhua Street, Shenyang 110819, China
Recommended Citation
GB/T 7714
Cao YZ,Liu, Meiju,Yang SK,et al. Target tracking based on neural network depth feature and texture fusion[C]. Bristol, UK:IOP,2019:1-6.
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