Joint Feature and Similarity Deep Learning for Vehicle Re-identification

doi:10.1109/ACCESS.2018.2862382

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	Joint Feature and Similarity Deep Learning for Vehicle Re-identification
	Zhu, Jianqing 1; Zeng, Huanqiang 2; Du, Yongzhao 1; Lei, Zhen3,4 ; Zheng, Lixin 1; Cai, Canhui 1
发表期刊	IEEE ACCESS
ISSN	2169-3536
	2018
卷号	6 页码:43724-43731
文章类型	Article
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.
关键词	Vehicle Re-identification Feature Representation Similarity Learning Deep Learning
WOS标题词	Science & Technology ; Technology
DOI	10.1109/ACCESS.2018.2862382
收录类别	SCI
语种	英语
项目资助者	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)
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000443980400001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：31[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/173211/27927
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
通讯作者	Zeng, Huanqiang
作者单位	1.Huaqiao Univ, Coll Engn, Fujian Prov Acad Engn Res Ctr Ind Intellectual Te, Qunzhou 362021, Peoples R China 2.Huaqiao Univ, Coll Informat Sci & Engn, Xiamen 361021, Peoples R China 3.Chinese Acad Sci, Ctr Biometr & Secur Res, Beijing 100190, 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.