融合子空间共享特征的多尺度跨模态行人重识别方法

王凤随; 闫涛; 刘芙蓉; 钱亚萍; 许月

doi:10.11999/JEIT211212

融合子空间共享特征的多尺度跨模态行人重识别方法

doi: 10.11999/JEIT211212

1.
安徽工程大学电气工程学院芜湖 241000
2.
检测技术与节能装置安徽省重点实验室芜湖 241000
3.
高端装备先进感知与智能控制教育部重点实验室芜湖 241000

基金项目: 安徽省自然科学基金(2108085MF197, 1708085MF154)，安徽高校省级自然科学研究重点项目(KJ2019A0162)，检测技术与节能装置安徽省重点实验室开放基金资助项目(DTESD2020B02)，安徽高校研究生科学研究项目(YJS20210448, YJS20210449)

详细信息

作者简介:
王凤随：男，博士，副教授，硕士生导师，研究方向为图像与视频信息处理、计算机视觉等

闫涛：男，硕士生，研究方向为图像处理与模式识别等

刘芙蓉：女，硕士生，研究方向为图像处理与模式识别等

钱亚萍：女，硕士生，研究方向为图像处理与模式识别等

许月：女，硕士生，研究方向为图像处理与模式识别等

通讯作者:
王凤随　fswang@ahpu.edu.cn

中图分类号: TN911.73; TP391.4
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- 被引次数: 0
出版历程
- 收稿日期: 2021-11-02
- 修回日期: 2022-03-25
- 录用日期: 2022-03-25
- 网络出版日期: 2022-03-30
- 刊出日期: 2023-01-17

Multi-scale Cross-Modality Person Re-identification Method Based on Shared Subspace Features

1.
School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
2.
Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Wuhu 241000, China
3.
Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Wuhu 241000, China

Funds: The Natural Science Foundation of Anhui Province, China (2108085MF197, 1708085MF154), The Natural Science Foundation of the Anhui Higher Education Institutions of China (KJ2019A0162), The Open Research Fund of Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University (DTESD2020B02), The Graduate Science Foundation of the Anhui Higher Education Institutions of China (YJS20210448, YJS20210449)

摘要

摘要: 跨模态行人重识别(Re-ID)是智能监控系统所面临的一项具有很大挑战的问题，现有的跨模态研究方法中主要基于全局或局部学习表示有区别的模态共享特征。然而，很少有研究尝试融合全局与局部的特征表示。该文提出一种新的多粒度共享特征融合(MSFF)网络，该网络结合了全局和局部特征来学习两种模态的不同粒度表示，从骨干网络中提取多尺度、多层次的特征，全局特征表示的粗粒度信息与局部特征表示的细粒度信息相互协同，形成更具有区别度的特征描述符。此外，为使网络能够提取更有效的共享特征，该文还针对网络中的两种模态的嵌入模式提出了子空间共享特征模块的改进方法，改变传统模态特征权重的特征嵌入方式。将该模块提前放入骨干网络中，使两种模态的各自特征映射到同一子空间中，经过骨干网络产生更丰富的共享权值。在两个公共数据集实验结果证明了所提方法的有效性，SYSU-MM01数据集最困难全搜索单镜头模式下平均精度mAP达到了60.62%。
- 行人重识别 /
- 跨模态 /
- 全局和局部特征 /
- 多粒度共享特征融合 /
- 子空间共享特征
Abstract: Cross-modal person Re-IDentification (Re-ID) is a challenging problem for intelligent surveillance systems, and existing cross-modal research approaches are mainly based on global or local learning representation of differentiated modal shared features. However, few studies have attempted fuse global and local feature representations. A new Multi-granularity Shared Feature Fusion (MSFF) network is proposed in this paper, which combines global and local features to learn different granularities representations of the two modalities, extracting multi-scale and multi-level features from the backbone network, where the coarse granularity information of the global feature representation and the fine granularity information of the local feature representation collaborate with each other to form more differentiated feature descriptors. In addition, in order to extract more effective shared features for the network, the paper also proposes an improved method of subspace shared feature module for embedding modes of the two modalities in the network, changing the feature embedding mode of traditional modal feature weights. The module is put into the backbone network in advance so that the respective features of the two modalities are mapped into the same subspace to generate richer shared weights through the backbone network. The experimental results in two public datasets demonstrate the effectiveness of the proposed method, and the average accuracy mAP in the most difficult full-search single-shot mode of SYSU-MM01 dataset reaches 60.62%.
- Person Re-IDentification (Re-ID) /
- Cross-modality /
- Global and local features /
- Multi-granularity Shared Feature Fusion (MSFF) /
- Subspace sharing features

HTML全文

图 1 行人从粗粒度到细粒度开始详细划分

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图 2 本文算法整体网络结构

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图 3 SYSU-MM01数据集中示例图像

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图 4 在SYSU-MM01的All-search single模式下和其它方法对比

下载: 全尺寸图片幻灯片

图 5 不同λ和子空间共享特征模块在MSFF网络中不同位置的性能实验结果

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表 1 网络3个分支的结构设置

Branch	Part	Map size	Dims	Feature
Part-global	1	12×4	512	Fg
Part-1	1+2	24×8	512×2+512	Fg1/Fp1
Part-2	1+3	24×8	512×3+512	Fg2/Fp2

下载: 导出CSV

表 2 在SYSU-MM01的All-search模式下和其他方法对比实验结果(%)

方法	单镜头				多镜头
方法	R-1	R-10	R-20	mAP	R-1	R-10	R-20	mAP
One-Stream^[17]	12.04	49.68	66.74	13.67	19.13	58.14	75.05	8.59
Two-stream^[17]	11.65	47.99	65.50	12.85	16.33	58.35	74.46	8.03
Zero-padding^[17]	14.80	54.12	71.33	15.95	–	61.40	78.41	10.89
TONE^[5]	12.52	50,72	68.60	14.42	–	–	–	–
HCML^[5]	14.32	53.16	69.17	16.16	–	–	–	–
BDTR^[16]	27.32	66.96	81.7	27.32	–	–	–	–
eBDTR^[18]	27.82	67.34	81.34	28.42	–	–	–	–
D²RL^[19]	28.90	70.60	82.40	29.20	–	–	–	–
MAC^[20]	33.26	79.04	90.09	36.22	–	–	–	–
DPMBN^[21]	37.02	79.46	89.87	40.28	–	–	–	–
AlignGAN^[22]	42.40	85.00	93.70	40.70	51.50	89.40	95.70	33.90
LZM^[23]	45.00	89.06	–	45.94	–	–	–	–
Hi-CMD^[24]	34.94	77.58	–	35.94	–	–	–	–
AGW^[25]	47.50	84.39	92.14	47.65	–	–	–	–
Xmodal^[26]	49.92	89.79	95.96	50.73	47.56	88.13	95.98	36.08
DDAG^[27]	54.75	90.39	95.81	53.02	–	–	–	–
cm-SSFT^[28]	67.60	89.20	93.90	63.20	64.4	91.2	95.7	62.0
Baseline(TSLFN)^[8]	59.96	91.50	96.82	54.95	62.09	93.74	97.85	48.02
本文	62.93	93.68	97.67	60.62	68.42	95.71	98.22	54.51

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表 3 在RegDB数据集和其它方法对比实验结果(%)

方法	Visible to Infrared				Infrared to Visible
方法	R-1	R-10	R-20	mAP	R-1	R-10	R-20	mAP
Zero-padding^[17]	17.75	34.21	44.35	18.90	16.63	34.68	44.25	17.82
HCML^[5]	24.44	47.53	56.78	20.08	21.70	45.02	55.58	22.24
BDTR^[16]	33.56	58.61	67.43	32.76	32.92	58.46	68.43	31.96
eBDTR^[18]	34.62	58.96	68.72	33.46	34.21	58.74	68.64	32.49
AlignGAN^[22]	57.90	–	–	53.60	56.30	–	–	53.40
MAC^[20]	36.43	62.36	71.63	37.03	36.20	61.68	70.99	36.63
Xmodal^[26]	62.21	83.13	91.72	60.18	–	–	–	–
DDAG^[27]	69.34	86.19	91.49	63.46	68.06	85.15	90.31	61.80
cm-SSFT*^[28]	72.30	–	–	72.90	71.00	–	–	71.70
Baseline(TSLFN)^[8]	–	–	–	–	–	–	–	–
本文	78.06	91.36	96.12	72.43	–	–	–	–

下载: 导出CSV

表 4 网络各个模块在SYSU-MM01数据集All-search single模式下实验结果(%)

	子空间共享特征模块	Part-global	Part-1	Part-2	R-1	R-10	R-20	mAP
P	×	√	√	√	56.67	91.85	97.14	55.27
Pg-1	√	×	√	√	61.63	93.02	97.51	58.41
Pg-2	√	√	×	√	61.63	92.63	96.92	57.83
Pg-3	√	√	√	×	57.44	91.53	97.01	55.28
MSFF	√	√	√	√	62.93	93.68	97.67	60.62

下载: 导出CSV

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