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Joint Feature and Similarity Deep Learning for Vehicle Re-identification

文献类型：期刊论文

作者Zhu, Jianqing2; Zeng, Huanqiang3; Du, Yongzhao2; Lei, Zhen1,4; Zheng, Lixin2; Cai, Canhui2
刊名IEEE ACCESS
出版日期2018
卷号6页码:43724-43731
关键词Vehicle Re-identification Feature Representation Similarity Learning Deep Learning
ISSN号2169-3536
DOI10.1109/ACCESS.2018.2862382
文献子类Article
英文摘要In this paper, a joint feature and similarity deep learning (JFSDL) method for vehicle reidentification is proposed. The proposed JFSDL method applies a siamese deep network to extract deep learning features for an input vehicle image pair simultaneously. The siamese deep network is learned under the joint identification and verification supervision. The joint identification and verification supervision is realized by linearly combining two softmax functions and one hybrid similarity learning function. Moreover, based on the hybrid similarity learning function, the similarity score between the input vehicle image pair is also obtained by simultaneously projecting the element-wise absolute difference and multiplication of the corresponding deep learning feature pair with a group of learned weight coefficients. Extensive experiments show that the proposed JFSDL method is superior to multiple state-of-the-art vehicle re-identification methods on both the VehicleID and VeRi data sets.
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000443980400001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China(61602191 ; Natural Science Foundation of Fujian Province(2018J01090 ; Science and Technology Bureau of Quanzhou(2017G027 ; Science and Technology Bureau of Xiamen(3502Z20173045) ; Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University(ZQN-PY418 ; Scientific Research Funds of Huaqiao University(16BS108 ; 61672521 ; 2016J01308) ; 2017G036) ; ZQN-YX403) ; 14BS201 ; 61375037 ; 14BS204) ; 61473291 ; 61572501 ; 61572536 ; 61502491 ; 61372107 ; 61401167)
源URL[http://ir.ia.ac.cn/handle/173211/27926]  
专题自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
通讯作者Zeng, Huanqiang
作者单位1.Chinese Acad Sci, Ctr Biometr & Secur Res, Beijing 100190, Peoples R China
2.Huaqiao Univ, Coll Engn, Fujian Prov Acad Engn Res Ctr Ind Intellectual Te, Qunzhou 362021, Peoples R China
3.Huaqiao Univ, Coll Informat Sci & Engn, Xiamen 361021, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Jianqing,Zeng, Huanqiang,Du, Yongzhao,et al. Joint Feature and Similarity Deep Learning for Vehicle Re-identification[J]. IEEE ACCESS,2018,6:43724-43731.
APA Zhu, Jianqing,Zeng, Huanqiang,Du, Yongzhao,Lei, Zhen,Zheng, Lixin,&Cai, Canhui.(2018).Joint Feature and Similarity Deep Learning for Vehicle Re-identification.IEEE ACCESS,6,43724-43731.
MLA Zhu, Jianqing,et al."Joint Feature and Similarity Deep Learning for Vehicle Re-identification".IEEE ACCESS 6(2018):43724-43731.

入库方式： OAI收割

来源：自动化研究所

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