ProAlign: Progressive Positional and Prototype-Guided Alignment for Aerial-Ground Person Re-Identification

Qiao Li; Jing Chen; Cong Wu; Kun He; Ruiying Du; Lei Tan

cs.CVNov 3, 2026

ProAlign: Progressive Positional and Prototype-Guided Alignment for Aerial-Ground Person Re-Identification

Authors: Qiao Li, Jing Chen, Cong Wu, Kun He, Ruiying Du, Lei Tan

Organizations: School of Cyber Science and Engineering, Wuhan University · National University of Singapore

Abstract

Aerial-Ground Person Re-Identification (AG-ReID) aims to match pedestrian images captured by unmanned aerial vehicles (UAVs) and ground-based surveillance cameras. The task remains highly challenging due to severe viewpoint discrepancies, frequent occlusions, and substantial domain gaps between aerial and ground imagery. Beyond these observable factors, existing methods often overlook two fundamental structural issues: cumulative positional drift during hierarchical feature extraction and semantic inconsistency across cross-view feature distributions. To address these challenges, we propose ProAlign, a Progressive Positional and Prototype-Guided Alignment Network for AG-ReID. ProAlign comprises two key components: a Layer-wise Progressive Positional Embedding (LPPE) module and a View-Aware Prototype Contrastive Learning (PCL) module. LPPE performs adaptive positional calibration throughout transformer layers by employing conditional positional encoding in shallow layers to mitigate local spatial distortions, while introducing learnable static positional embeddings in deeper layers to reinforce global semantic priors. Meanwhile, PCL maintains view-specific aerial and ground prototypes for each identity and enforces cross-view semantic consistency via a dual-prototype contrastive objective. Extensive experiments on CARGO and two real-world AG-ReID benchmarks demonstrate the effectiveness of ProAlign, achieving state-of-the-art overall retrieval performance on CARGO with improvements of 1.60% in Rank-1 accuracy and 5.01% in mAP over the strongest competing methods.

Figures & tables

Figure 1: Illustration of the motivation of ProAlign. (a) Prior methods suffer from cumulative positional drift and cross-view distributional gaps. (b) ProAlign addresses these issues through layer-wise positional calibration (LPPE) and prototype-guided semantic alignment (PCL).

Figure 2: Overview of the ProAlign architecture. Given aerial or ground-view inputs, ProAlign first extracts patch tokens and processes them through transformer layers equipped with LPPE for depth-adaptive spatial correction. In parallel, the PCL module maintains view-specific prototypes to facilitate cross-view identity alignment.

Method	Protocol 1: ALL			Protocol 2: G $\leftrightarrow$ G			Protocol 3: A $\leftrightarrow$ A			Protocol 4: A $\leftrightarrow$ G
Method	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP
SBS He et al. (2023)	50.32	43.09	29.76	72.31	62.99	48.24	67.50	49.73	29.32	31.25	29.00	18.71
PCB Sun et al. (2019)	51.00	44.50	32.20	74.10	67.60	55.10	55.00	44.60	27.00	34.40	30.40	20.10
BoT Luo et al. (2019)	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 Wang et al. (2018)	54.81	49.08	36.52	83.93	71.05	55.20	65.00	52.96	36.78	31.87	33.47	24.64
VV Kuma et al. (2019) ; Kumar et al. (2020)	45.83	38.84	39.57	72.31	62.99	48.24	67.50	49.73	29.32	31.25	29.00	18.71

Table 1: Performance comparison of the mainstream methods under four settings of the CARGO dataset . “ALL” denotes the overall retrieval performance of each method. “G

\leftrightarrow

G”, “A

\leftrightarrow

A”, and “A

\leftrightarrow

G” represent the performance of each model in several specific retrieval patterns. Rank1, mAP, and mINP are reported (%). The best performance is shown in bold .

Setting	Protocol 1: ALL			Protocol 2: G $\leftrightarrow$ G			Protocol 3: A $\leftrightarrow$ A			Protocol 4: A $\leftrightarrow$ G
Setting	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP
Baseline	64.10	55.20	41.13	82.14	71.59	58.39	82.50	66.83	50.22	48.12	42.75	29.95
Baseline + LPPE	60.58	54.92	43.13	79.46	74.10	65.12	75.00	63.62	47.28	53.19	52.63	44.73
Baseline + PCL	65.38	58.55	46.49	79.46	73.99	64.52	82.50	67.66	51.20	58.51	57.65	49.68
Baseline + LPPE + PCL	68.27	62.47	51.39	84.82	80.22	72.51	85.00	70.05	54.66	60.64	58.44	49.86

Table 2: Ablation study of the different components in ProAlign on the CARGO dataset. “LPPE” denotes the Layer-wise Positional Embedding module. “PCL” refers to the Prototype Contrastive Learning module. The best performance per metric is shown in bold .

Setting	Protocol 1: ALL			Protocol 2: G $\leftrightarrow$ G			Protocol 3: A $\leftrightarrow$ A			Protocol 4: A $\leftrightarrow$ G
Setting	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP
Depths of CPE
First 3 layers	68.59	60.97	48.7	85.71	77.74	67.86	80.00	68.32	53.99	60.64	60.31	52.22
First 6 layers	68.27	62.47	51.39	84.82	80.22	72.51	85.00	70.05	54.66	60.64	58.44	49.86
First 9 layers	66.35	60.40	48.43	81.25	76.22	67.17	82.50	68.73	53.71	62.77	61.66	53.87
All layers	67.63	58.87	45.41	83.93	74.92	64.11	80.00	67.58	52.16	61.70	59.60	50.12

Table 3: Performance comparison under different depths of CPE in LPPE. Results are reported under four matching protocols on the CARGO dataset. The best values per column are shown in bold .

Setting	Protocol 1: ALL			Protocol 2: G $\leftrightarrow$ G			Protocol 3: A $\leftrightarrow$ A			Protocol 4: A $\leftrightarrow$ G
Setting	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP
$\gamma=0$	66.67	57.24	42.85	82.14	73.79	62.44	80.00	64.44	46.03	63.83	57.99	46.82
$\gamma=0.25$	67.31	58.54	45.12	82.14	75.25	65.02	85.00	68.98	51.75	62.77	60.54	51.29
$\gamma=0.5$	68.27	62.47	51.39	84.82	80.22	72.51	85.00	70.05	54.66	60.64	58.44	49.86
$\gamma=0.75$	66.67	58.54	44.67	82.14	73.25	61.65	82.50	68.39	53.43	59.57	58.76	49.85

Table 4: Impact of the cross-view alignment weight

\gamma

in PCL. Results are reported under four matching protocols on the CARGO dataset. The best values per column are shown in bold .

Different variants of prototype
Setting	Protocol 1: ALL			Protocol 2: G $\leftrightarrow$ G			Protocol 3: A $\leftrightarrow$ A			Protocol 4: A $\leftrightarrow$ G
Setting	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP
Inner-Batch	62.82	56.12	43.55	77.68	72.57	62.64	77.50	64.44	48.94	60.64	58.50	49.49
Global Memory	61.86	55.40	42.71	77.68	71.83	61.10	77.50	64.53	48.89	56.38	55.80	47.80
Classifier Weights	62.82	56.30	43.98	81.25	75.06	65.08	75.00	63.90	47.79	53.19	53.52	45.54
View-Aware PCL	68.27	62.47	51.39	84.82	80.22	72.51	85.00	70.05	54.66	60.64	58.44	49.86

Table 5: Comparison between different variants of prototype. Results are reported under four matching protocols on the CARGO dataset. The best values per column are shown in bold .

Setting	Params (M)	GFLOPs	Latency (ms)	$\Delta$ Params	$\Delta$ Latency
VDT	85.97	60.75	11.19	–	–
GSAlign	111.67	76.92	11.44	+29.9%	+2.23%
ProAlign	86.98	74.69	11.25	+1.17%	+0.54%

Table 6: Computational complexity comparison. Latency is measured as the average forward pass time over 200 runs.

Layer	3	4	5	6	7	8	9	10	11
VDT	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
LPPE-0.25	0.982	0.982	0.977	0.951	0.881	0.851	0.864	0.857	0.986
LPPE-0.5	0.983	0.982	0.960	0.868	0.809	0.839	0.837	0.865	0.931
LPPE-0.75	0.982	0.985	0.958	0.861	0.817	0.842	0.860	0.881	0.941
LPPE-1.0	0.982	0.982	0.973	0.951	0.871	0.850	0.862	0.845	0.974

Table 7: Normalized [CLS]-attention entropy

\mathcal{H}^{(l)}

per layer on CARGO. 1.0 denotes uniform attention over all 128 patches; lower values indicate sharper spatial focus. LPPE-

r

applies CPE to the first

12r

layers (LPPE-0.5 is the default ProAlign setting).

Figure 3: t-SNE visualization of feature distributions. $\triangle$ and $\bigcirc$ denote aerial and ground features, respectively. Baseline shows view-dependent clustering with fragmented identity distributions, while ProAlign produces more compact identity-wise clusters and reduces cross-view discrepancy.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 4: Attention map comparison between baseline and ProAlign. In both aerial and ground views, the baseline attention is scattered due to severe viewpoint distortion, while LPPE produces more focused responses on the person.

Setting	Protocol 1: A $\leftrightarrow$ G			Protocol 2: G $\leftrightarrow$ A
Setting	Rank1	mAP	mINP	Rank1	mAP	mINP
SBS [ 38 ]	73.54	59.77	-	73.70	62.27	-
BoT [ 34 ]	70.01	55.47	-	71.20	58.83	-
OSNet [ 41 ]	72.59	58.32	-	74.22	60.99	-
VV [ 39 , 40 ]	77.22	67.23	41.43	79.73	69.83	42.37
Explain [ 4 ]	81.47	72.61	-	82.85	73.39	-

Appendix

Table 8: Performance comparison of the mainstream methods under two settings of the AG-ReID dataset. Results are reported under matching protocols. The best values per column are shown in bold .

Setting	Protocol 1: A $\leftrightarrow$ G		Protocol 2: G $\leftrightarrow$ A		Protocol 3: A $\leftrightarrow$ W		Protocol 4: W $\leftrightarrow$ A
Setting	Rank1	mAP	Rank1	mAP	Rank1	mAP	Rank1	mAP
BoT [ 34 ]	85.40	77.03	84.65	75.90	89.77	80.48	84.65	76.90
Explain [ 4 ]	87.70	79.00	87.35	78.24	93.67	83.14	87.73	79.08
VDT [ 6 ]	86.46	79.13	86.14	78.12	90.00	82.21	85.26	78.52
AG-ReIDv2 [ 10 ]	88.77	80.72	87.86	78.51	93.62	84.85	88.61	80.11
SeCap [ 30 ]	88.12	80.84	88.24	79.99	91.44	84.01	87.56	80.15

Appendix

Table 9: Performance comparison of the mainstream methods under four settings of the AG-ReID v2 dataset. Results are reported under four matching protocols. The best values per column are shown in bold .

Setting	Protocol 1: ALL			Protocol 2: G $\leftrightarrow$ G			Protocol 3: A $\leftrightarrow$ A			Protocol 4: A $\leftrightarrow$ G
Setting	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP	Rank1	mAP	mINP
$m=0.1$	67.63	60.75	47.97	81.25	75.57	66.16	80.00	67.50	51.37	67.02	62.61	52.69
$m=0.2$	68.27	62.47	51.39	84.82	80.22	72.51	85.00	70.05	54.66	60.64	58.44	49.86
$m=0.3$	67.31	59.24	45.95	83.93	75.79	65.32	82.50	67.00	49.45	64.89	61.42	51.48

Appendix

Table 10: Impact of the momentum coefficient (

m

) for prototype update in PCL. Results are reported under four matching protocols on the CARGO dataset. The best values per column are shown in bold .

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Open paper AskPDF arXiv

Aerial-Ground Person Re-Identification (AGPReID) remains highly challenging due to drastic viewpoint variations between drones and fixed cameras. Existing methods typically follow a view-invariant paradigm, aligning shared features across views to achieve robustness. However, view-invariant inherently enforces part-level alignment, which ignores view-specific cues and discriminative identity information. To this end, this work proposes ViSA (View-aware Semantic Alignment), a view-aware framework that achieves cross-view semantic consistency containing an Expert-driven Token Generation Module (ETGM) and a Dual-branch Local Fusion Module (DLFM). Technically, the former constructs a set of view-aware experts to generate adaptive semantic queries that perceive viewpoint-specific patterns, while the latter leverages graph reasoning to extract and align local regions responsive to different experts. Extensive experiments on three AGPReID benchmarks including AG-ReID.v2, CARGO and LAGPeR demonstrate that ViSA consistently achieves superior performance, with a notable 10.06% mAP improvement on the challenging CARGO cross-view protocol. The code is available at https://github.com/Cat-Zero/ViSA.

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