Vehicle Re-Identification Using Quadruple Directional Deep Learning Features

doi:10.1109/TITS.2019.2901312

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 生物识别与安全技术研究中心

	Vehicle Re-Identification Using Quadruple Directional Deep Learning Features
	Zhu, Jianqing 1,2; Zeng, Huanqiang 4; Huang, Jingchang 5,6; Liao, Shengcai 3; Lei, Zhen 3; Cai, Canhui 1,2; Zheng, Lixin 1,2
刊名	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2020
卷号	21 期号:1 页码:410-420
关键词	Deep learning Feature extraction Convolutional neural networks Databases Measurement Cameras Intelligent transportation systems Computer vision artificial neural networks feature extraction image classification
ISSN号	1524-9050
DOI	10.1109/TITS.2019.2901312
通讯作者	Zeng, Huanqiang(zeng0043@hqu.edu.cn)
英文摘要	In order to resist the adverse effect of viewpoint variations, we design quadruple directional deep learning networks to extract quadruple directional deep learning features (QD-DLF) of vehicle images for improving vehicle re-identification performance. The quadruple directional deep learning networks are of similar overall architecture, including the same basic deep learning architecture but different directional feature pooling layers. Specifically, the same basic deep learning architecture that is a shortly and densely connected convolutional neural network is utilized to extract the basic feature maps of an input square vehicle image in the first stage. Then, the quadruple directional deep learning networks utilize different directional pooling layers, i.e., horizontal average pooling layer, vertical average pooling layer, diagonal average pooling layer, and anti-diagonal average pooling layer, to compress the basic feature maps into horizontal, vertical, diagonal, and anti-diagonal directional feature maps, respectively. Finally, these directional feature maps are spatially normalized and concatenated together as a quadruple directional deep learning feature for vehicle re-identification. The extensive experiments on both VeRi and VehicleID databases show that the proposed QD-DLF approach outperforms multiple state-of-the-art vehicle re-identification methods.
资助项目	National Natural Science Foundation of China[61602191] ; National Natural Science Foundation of China[61871434] ; National Natural Science Foundation of China[61802136] ; National Natural Science Foundation of China[61605048] ; National Natural Science Foundation of China[61876178] ; Natural Science Foundation of Fujian Province[2018J01090] ; Natural Science Foundation for Outstanding Young Scholars of Fujian Province[2019J06017] ; Science and Technology Bureau of Quanzhou[2018C115R] ; Science and Technology Bureau of Quanzhou[2017G027] ; Science and Technology Bureau of Quanzhou[2017G036] ; 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] ; Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University[ZQN-YX403] ; Scientific Research Funds of Huaqiao University[16BS108] ; Scientific Research Funds of Huaqiao University[14BS201] ; Scientific Research Funds of Huaqiao University[14BS204]
WOS研究方向	Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000506619900032
资助机构	National Natural Science Foundation of China ; Natural Science Foundation of Fujian Province ; Natural Science Foundation for Outstanding Young Scholars of Fujian Province ; Science and Technology Bureau of Quanzhou ; Science and Technology Bureau of Xiamen ; Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University ; Scientific Research Funds of Huaqiao University
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/29535]
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.Huaqiao Univ, Coll Engn, Quanzhou 362021, Peoples R China 2.Fujian Prov Acad Engn, Res Ctr Ind Intellectual Tech, Quanzhou 362021, Peoples R China 3.Chinese Acad Sci, Inst Automat, Ctr Biometr & Secur Res, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 4.Huaqiao Univ, Coll Informat Sci & Engn, Xiamen 361021, Peoples R China 5.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Sci & Technol Microsyst Lab, Beijing 201800, Peoples R China 6.IBM Res China, Shanghai 201203, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Jianqing,Zeng, Huanqiang,Huang, Jingchang,et al. Vehicle Re-Identification Using Quadruple Directional Deep Learning Features[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2020,21(1):410-420.
APA	Zhu, Jianqing.,Zeng, Huanqiang.,Huang, Jingchang.,Liao, Shengcai.,Lei, Zhen.,...&Zheng, Lixin.(2020).Vehicle Re-Identification Using Quadruple Directional Deep Learning Features.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,21(1),410-420.
MLA	Zhu, Jianqing,et al."Vehicle Re-Identification Using Quadruple Directional Deep Learning Features".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 21.1(2020):410-420.