车辆再识别技术综述

doi:10.11959/j.issn.2096-6652.202002

Abstract

Abstract:

Given a vehicle image,vehicle re-identification aims to find the same vehicle caught by other cameras,it can be regarded as a sub-problem of image retrieval.In the real traffic surveillance system,vehicle re-identification can play a role in locating,supervising and criminal investigation of target vehicles.With the rise of deep neural networks and the release of large-scale dataset,improving the accuracy and efficiency of vehicle re-identification has become a research focus in the field of computer vision and multimedia in recent years.The vehicle re-identification methods from different perspectives were classified,and the overview,comparison and analysis in terms of feature extraction,design and performance were given in detail,and the challenges and future trends of vehicle re-identification were predicted.

Key words: vehicle re-identification, deep learning, feature representation, metric learning

CLC Number:

TP520

Kai LIU,Yidong LI,Weipeng LIN. A survey on vehicle re-identification[J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(1): 10-25.

Figures/Tables 7

References 71

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分类依据	方法类别	参考文献	优点	缺点
实现方式	基于传感器的方法	参考文献[11-21]等	不需要训练学习	复现难度高；识别率低；需要大量硬件
	基于人工设计特征的方法	参考文献[22-33]等	不依赖特殊硬件；可解释性强	时间复杂度高；受光照变化、视角变化和遮挡的影响较大
	基于深度学习的方法	参考文献[34-68]等	识别率高；实时性强；可以解决光照变化、视角变化和遮挡问题	训练学习时间长；特征抽象，可解释性差
解决车辆再识别问题的着手点	利用多维度信息的方法	参考文献[39-48]等	对车辆特殊外观敏感	容易误判辨识性区域；易受视角、光照变化影响
	基于度量学习的方法	参考文献[49-54]等	识别率高；对难样本的识别效果好	训练学习时间长
	解决视角变化引起的外观偏差	参考文献[55-58]等	对车辆的视角变化有鲁棒性；识别率高	训练学习时间长；系统实时性难以得到保证
	利用时序地理信息的方法	参考文献[59]等	对难样本的识别效果好；方法新颖	需要车辆时序和位置标签；对数据集要求高
是否需要车辆身份标签	有监督学习方法	参考文献[34-65]等	识别率高；符合发展潮流；在不同数据集下的泛化能力强	训练学习时间长；需要大型数据集支持
	无监督学习方法	参考文献[66-68]等	无需人工标注；符合实际需求；拓展性强	识别率较低；时间复杂度高

方法	参考文献序号	评价指标
方法	参考文献序号	mAP	Rank-1	Rank-5
FACT	[6]	19.9	59.6	75.2
XVGAN	[64]	24.7	60.2	77.0
FACT+SNN+STR	[6]	27.7	61.4	78.7
OIFE	[10]	51.4	68.3	89.7
PROVID	[39]	53.4	81.5	95.1
FDA-Net	[63]	55.4	84.2	92.4
VGG+C+T+S	[49]	57.4	86.5	92.8
S-CNN+PathLSTM	[59]	58.2	83.5	90.0
GS-TRE	[51]	59.4	96.2	98.9
VAMI	[56]	61.3	85.9	91.8
AAVER	—	61.1	90.1	94.7
RAM	[65]	61.5	88.6	94.0
VANet	[57]	66.3	89.7	95.9
PNVR	[43]	74.3	94.3	98.7
MRL	[58]	78.5	94.3	99.0
PGAN	—	79.3	96.5	98.3
PRN	[54]	85.8	97.1	99.4

方法	参考文献序号			评价指标
		小尺寸		中尺寸		大尺寸
		Rank-1	Rank-5	Rank-1	Rank-5	Rank-1	Rank-5
DRDL	[8]	49.0%	73.5%	42.8%	66.8%	38.2%	61.6%
C2F-Rank	[50]	61.1%	81.7%	56.2%	76.2%	51.4%	72.2%
VAMI	[56]	63.1%	83.2%	52.8%	75.1%	47.3%	70.2%
FDA-Net	[63]	64.0%	82.8%	57.8%	78.3%	49.4%	70.4%
VGG+C+T+S	[49]	69.9%	87.3%	66.2%	82.3%	63.2%	79.4%
OIFE	[10]	—	—	—	—	67.0%	82.9%
GS-TRE	[51]	75.9%	84.2%	74.8%	83.6%	74.0%	82.7%
AAVER	—	74.6%	93.8%	68.6%	89.9%	63.5%	85.6%
PNVR	[43]	78.4%	92.3%	75.0%	88.3%	74.2%	86.4%
PGAN	—	—	—	—	—	77.8%	92.1%
PRN	[54]	78.9%	94.8%	74.9%	92.0%	71.5%	88.4%
VANet	[57]	83.2%	95.9%	81.1%	94.7%	77.2%	86.7%
MRL	[58]	84.8%	96.9%	80.9%	94.1%	78.4%	92.1%

A survey on vehicle re-identification

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