基于隐式解码对齐的空地行人重识别方法

贝俊仁; 张权; 赖剑煌

doi:10.16383/j.aas.c240705

基于隐式解码对齐的空地行人重识别方法

doi: 10.16383/j.aas.c240705 cstr: 32138.14.j.aas.c240705

贝俊仁^1,,
张权^2,,
赖剑煌^{1, 3, 4, 5,}

1.
中山大学计算机学院广州 510006
2.
中山大学系统科学与工程学院广州 510275
3.
琶洲实验室(黄埔) 广州 510006
4.
广东省信息安全技术重点实验室广州 510006
5.
机器智能与先进计算教育部重点实验室广州 510006

基金项目: 国家自然科学基金(U22A2095), 广州市重点研发计划(202206030003), 广东省信息安全技术重点实验室项目(No. 2023B1212060026)资助

详细信息

作者简介:
贝俊仁：中山大学计算机学院硕士研究生. 主要研究方向为行人重识别与计算机视觉. E-mail: beijr@mail2.sysu.edu.cn

张权：中山大学系统科学与工程学院博士后. 主要研究方向为行人重识别与计算机视觉. 本文通讯作者. E-mail: zhangq689@mail.sysu.edu.cn

赖剑煌：中山大学计算机学院教授. 主要研究方向为计算机视觉与模式识别. E-mail: stsljh@mail.sysu.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2024-10-31
- 录用日期: 2025-05-29
- 网络出版日期: 2025-08-01

Implicit Decoder Alignment for Aerial-ground Person Re-identification

BEI Jun-Ren^1
,,
ZHANG Quan^2
,,
LAI Jian-Huang^{1, 3, 4, 5
,}

1.
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006
2.
School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou 510275
3.
Pazhou Laboratory (Huangpu), Guangzhou 510006
4.
Guangdong Province Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou 510006
5.
Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Sun Yat-sen University, Guangzhou 510006

Funds: Supported by National Natural Science Foundation of China (U22A2095), Key-Area Research and Development Program of Guangzhou (202206030003), and the Project of Guangdong Provincial Key Laboratory of Information Security Technology (Grant No. 2023B1212060026).

More Information

Author Bio:
BEI Jun-Ren　Master candidate at the School of Computer Science and Engineering, Sun Yat-sen University. His research interest covers person re-identification and computer vision

ZHANG Quan　Postdoctoral Fellow at the School of Systems Science and Engineering, Sun Yat-sen University. His research interest covers person re-identification and computer vision. Corresponding author of the paper

LAI Jian-Huang　Professor at the School of Computer Science and Engineering, Sun Yat-sen University. His research interest covers computer vision and pattern recognition

摘要

摘要: 空地行人重识别任务旨在包含地面与空中视角的监控相机网络中, 实现对特定行人的精确识别与跨镜关联. 该任务的特有挑战在于克服空地成像设备之间巨大的视角差异对于学习判别性行人身份特征的干扰. 现有工作在行人特征建模方面存在不足, 未充分考虑跨视角特征对齐对识别与检索性能的提升作用. 基于此, 本文提出一种基于隐式特征对齐的空地行人重识别方法, 主要包含两方面的创新: 在模型设计方面, 提出基于自注意力解码器的隐式对齐框架, 通过在解码阶段利用一组可学习的口令特征挖掘行人判别部件区域, 并提取和对齐行人局部特征, 从而实现判别性行人表征的学习; 在优化目标方面, 提出正交性和一致性损失函数, 前者约束口令特征以多样化判别性行人部件为关注点, 后者缓解了跨视角特征表达的偏置分布. 在当前最大可用的空地重识别数据集CARGO上进行实验, 结果表明本文方法在检索性能上优于现有重识别方法, 实现显著的性能提升.
- 行人重识别 /
- 图像检索 /
- 图像识别 /
- 自注意力网络
Abstract: Aerial-ground person re-identification (AGPReID) refers to the accurate recognition and cross-camera association of the interest person within a surveillance camera network deployed across aerial and ground views. One of the unique challenges is overcoming the significant view differences between aerial and ground cameras, which interfere with discriminate feature learning of person identity. Existing methods are insufficient in modeling person features, as they fail to fully leverage cross-view feature alignment to enhance recognition and retrieval performance. To this end, the implicit decoder alignment is proposed for AGPReID with two main contributions: In terms of model design, we propose an implicit alignment framework based on a self-attention decoder. This framework leverages a set of learnable token features during the decoding stage to identify discriminative person parts and to extract and align local pedestrian features, enabling discriminative person representation learning. In terms of optimization objectives, we propose orthogonality and consistency loss functions. The former constrains token features to focus on diverse discriminative pedestrian parts, while the latter mitigates biased distributions in cross-view feature representations. Experiments were conducted on the largest publicly available cross-view re-identification dataset, CARGO, where our method outperformed existing re-identification methods, achieving superior retrieval performance.
- Person re-identification /
- image retrieval /
- image recognition /
- Transformer

HTML全文

图 1 空地行人重识别任务示意图

Fig. 1 Illustration of the aerial-ground person re-identification task

下载: 全尺寸图片幻灯片

图 2 隐式解码对齐框架示意图

Fig. 2 Illustration of the implicit decoder alignment framework (IDA)

下载: 全尺寸图片幻灯片

图 3 模型性能增益分析, 其中结果来自CARGO数据集的协议1

Fig. 3 Model performance gain analysis, where results come from Protocol 1 of the CARGO dataset

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图 4 IDA框架中的参数分析

Fig. 4 Parameter analysis in the IDA framework

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图 5 IDA框架在CARGO数据集四种协议下的可视化分析

Fig. 5 Retrieval visualization of IDA framework under the four protocols of the CARGO dataset

下载: 全尺寸图片幻灯片

表 1 常用符号及其含义

Table 1 Commonly used symbols and their meanings in our work

符号	含义
$ {\cal{D}},\;{\cal{D}}^{tr},\; {\cal{D}}^{te} $	空地重识别数据集, 训练集, 测试集
$ B $	批数据
$ x_i,\; y_i,\; v_i $	行人图像, 身份标签, 视角标签
$ {\cal{F}},\;{\cal{F}}_e,\; {\cal{F}}_d $	网络模型, 自注意力编码器和解码器
$ {\boldsymbol{Q}},\;{\boldsymbol{K}},\;{\boldsymbol{V}} $	注意力操作的查询、键值和内容
$ \theta,\;\theta_e,\; \theta_d $	$ {\cal{F}},\;{\cal{F}}_e,\; {\cal{F}}_d $中的可学习参数
$ {\cal{T}}(\cdot) $	输入离散口令化
$ t_g,\; t_a $	可学习口令特征
$ {\boldsymbol{L}} $	局部口令特征矩阵
$ {\boldsymbol{S}}_{g\leftrightarrow g},\; {\boldsymbol{S}}_{a\leftrightarrow a},\;{\boldsymbol{S}}_{a\leftrightarrow g} $	相似度矩阵
$ {\cal{L}}_g^c,\; {\cal{L}}_g^t $	全局特征损失函数
$ {\cal{L}}_a^c,\; {\cal{L}}_a^t,\; {\cal{L}}_a^o,\; {\cal{L}}_a^s $	局部特征损失函数
$ \left\|\cdot\right\| $	集合的阶

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表 2 CARGO数据集四种协议的主流方法性能评测. 汇报的指标包括Rank1, mAP, mINP(%). 最佳性能以加粗显示

Table 2 The performance evaluation of the mainstream methods for the four protocols of the CARGO dataset. Rank1, mAP, and mINP (%) are reported. Best performances are shown in bolded

方法	协议1:ALL			协议2:G$ \leftrightarrow $G			协议3:A$ \leftrightarrow $A			协议4:A$ \leftrightarrow $G
方法	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP
PCB^{[25, 26]}	44.23	38.15	26.14	72.32	61.92	45.72	57.50	42.34	22.50	21.25	21.02	14.22
SBS^[20]	50.32	43.09	29.76	73.21	62.99	48.24	67.50	49.73	29.32	31.25	29.00	18.71
BoT^[33]	54.81	46.49	32.40	77.68	66.47	51.34	65.00	49.79	29.82	36.25	32.56	21.46
MGN^[31]	54.49	46.58	33.55	82.14	69.31	53.60	65.00	48.86	27.42	32.50	30.44	21.53
APNet^[32]	58.97	50.24	35.76	77.68	66.83	51.85	67.50	54.57	37.35	44.37	39.35	26.76
VV^[56]	45.83	38.84	39.57	72.31	62.99	48.24	67.50	49.73	29.32	31.25	29.00	18.71
AGW^[2]	60.26	53.44	40.22	81.25	71.66	58.09	67.50	56.48	40.40	43.57	40.90	29.39
TransReID^[35]	60.90	53.17	39.57	—	—	—	—	—	—	—	—	—
VDT^[51]	64.10	55.20	41.13	82.14	71.59	58.39	82.50	66.83	50.22	48.12	42.76	29.95
基线模型	61.54	53.54	39.62	82.14	71.34	57.55	80.00	64.47	47.07	43.13	40.11	28.20
IDA	64.42	58.17	46.17	83.04	77.04	67.50	82.50	69.65	54.58	48.75	45.13	33.92

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表 3 IDA框架消融实验. 汇报的指标包括Rank1, mAP, mINP(%). 最佳性能以加粗显示

Table 3 Ablation study of IDA framework. Rank1, mAP, and mINP are reported (%). The best performance is shown in bolded

			协议1:ALL			协议2:G$ \leftrightarrow $G			协议3:A$ \leftrightarrow $A			协议4:A$ \leftrightarrow $G
$ {\cal{F}}(\cdot) $	$ {\cal{L}}_a^o $	$ {\cal{L}}_a^s $	Rank	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP
$\checkmark$			60.26	53.89	41.36	81.25	73.66	62.70	75.00	63.94	47.10	43.75	40.33	28.71
$\checkmark$	$\checkmark$		63.78	57.55	45.69	83.93	77.33	68.21	77.50	64.55	47.52	46.25	43.97	32.88
$\checkmark$		$\checkmark$	61.22	55.76	44.22	82.14	75.53	66.28	80.00	69.36	55.21	44.37	41.82	30.88
$\checkmark$	$\checkmark$	$\checkmark$	64.42	58.17	46.17	83.04	77.04	67.50	82.50	69.65	54.58	48.75	45.13	33.92

下载: 导出CSV

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