# AG-VPReID: A Challenging Large-Scale Benchmark for Aerial-Ground Video-based Person Re-Identification

arXiv: https://arxiv.org/abs/2503.08121 · HTML: https://mlsanity.com/paper/2503.08121/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 53 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 30 of 34)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Frozen CLIP Models are Efficient Video Learners](https://mlsanity.com/paper/2208.03550/) (2022)
- [CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels](https://mlsanity.com/paper/2211.13977/) (2022)
- [Clothes-Changing Person Re-identification with RGB Modality Only](https://mlsanity.com/paper/2204.06890/) (2022)
- [Improving Person Re-identification by Attribute and Identity Learning](https://mlsanity.com/paper/1703.07220/) (2017)
- [Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification](https://mlsanity.com/paper/2104.14913/) (2021)
- [Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset](https://mlsanity.com/paper/2211.01917/) (2022)
- [Temporal Complementary Learning for Video Person Re-Identification](https://mlsanity.com/paper/2007.09357/) (2020)
- [STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification](https://mlsanity.com/paper/1811.04129/) (2018)
- [Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions](https://mlsanity.com/paper/2306.07520/) (2023)
- [Watching You: Global-guided Reciprocal Learning for Video-based Person Re-identification](https://mlsanity.com/paper/2103.04337/) (2021)
- [BigGait: Learning Gait Representation You Want by Large Vision Models](https://mlsanity.com/paper/2402.19122/) (2024)
- [BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification](https://mlsanity.com/paper/2104.14783/) (2021)
- [Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-based Person Re-identification](https://mlsanity.com/paper/2003.12224/) (2020)
- [Multi-scale 3D Convolution Network for Video Based Person Re-Identification](https://mlsanity.com/paper/1811.07468/) (2018)
- [TF-CLIP: Learning Text-free CLIP for Video-based Person Re-Identification](https://mlsanity.com/paper/2312.09627/) (2023)
- [Video-based Person Re-identification with Spatial and Temporal Memory Networks](https://mlsanity.com/paper/2108.09039/) (2021)
- [3D Human Texture Estimation from a Single Image with Transformers](https://mlsanity.com/paper/2109.02563/) (2021)
- [Parameter-Efficient Person Re-identification in the 3D Space](https://mlsanity.com/paper/2006.04569/) (2020)
- [Spatial and Temporal Mutual Promotion for Video-based Person Re-identification](https://mlsanity.com/paper/1812.10305/) (2018)
- [3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning](https://mlsanity.com/paper/2007.13666/) (2020)
- [A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification](https://mlsanity.com/paper/2104.01745/) (2021)
- [PhysPT: Physics-aware Pretrained Transformer for Estimating Human Dynamics from Monocular Videos](https://mlsanity.com/paper/2404.04430/) (2024)
- [Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos](https://mlsanity.com/paper/2104.08241/) (2021)
- [Dense Interaction Learning for Video-based Person Re-identification](https://mlsanity.com/paper/2103.09013/) (2021)
- [View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network](https://mlsanity.com/paper/2403.14513/) (2024)
- [Deeply-Coupled Convolution-Transformer with Spatial-temporal Complementary Learning for Video-based Person Re-identification](https://mlsanity.com/paper/2304.14122/) (2023)
- [The State of Aerial Surveillance: A Survey](https://mlsanity.com/paper/2201.03080/) (2022)
- [AG-ReID.v2: Bridging Aerial and Ground Views for Person Re-identification](https://mlsanity.com/paper/2401.02634/) (2024)
- [Aerial-Ground Person Re-ID](https://mlsanity.com/paper/2303.08597/) (2023)

## Built on by (most cited first, 13 of 13)

- [LATex: Leveraging Attribute-based Text Knowledge for Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2503.23722/) (2025)
- [SD-ReID: View-aware Stable Diffusion for Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2504.09549/) (2025)
- [Person Recognition in Aerial Surveillance: A Decade Survey](https://mlsanity.com/paper/2511.17674/) (2025)
- [AG-VPReID 2025: Aerial-Ground Video-based Person Re-identification Challenge Results](https://mlsanity.com/paper/2506.22843/) (2025)
- [SAS-VPReID: A Scale-Adaptive Framework with Shape Priors for Video-based Person Re-Identification at Extreme Far Distances](https://mlsanity.com/paper/2601.05535/) (2026)
- [Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey](https://mlsanity.com/paper/2510.09731/) (2025)
- [Seeing Across Time and Views: Multi-Temporal Cross-View Learning for Robust Video Person Re-Identification](https://mlsanity.com/paper/2511.02564/) (2025)
- [IDSelect: A RL-Based Cost-Aware Selection Agent for Video-based Multi-Modal Person Recognition](https://mlsanity.com/paper/2602.18990/) (2026)
- [Causality and "In-the-Wild" Video-Based Person Re-ID: A Survey](https://mlsanity.com/paper/2505.20540/) (2025)
- [S3-CLIP: Video Super Resolution for Person-ReID](https://mlsanity.com/paper/2601.08807/) (2026)
- [Cross-modal Fuzzy Alignment Network for Text-Aerial Person Retrieval and A Large-scale Benchmark](https://mlsanity.com/paper/2603.20721/) (2026)
- [Rethinking Air-Ground Collaboration: A Progressive Cross-Task Benchmark and Socialized Learning Framework](https://mlsanity.com/paper/2606.18841/) (2026)
- [The Gap Is in the Geometry: A Convergent Meta-Analysis of Multi-Facet Aerial and Remote Sensing Video Understanding](https://mlsanity.com/paper/2610.19102/) (2026)

## Abstract (arXiv)

We introduce AG-VPReID, a new large-scale dataset for aerial-ground video-based person re-identification (ReID) that comprises 6,632 subjects, 32,321 tracklets and over 9.6 million frames captured by drones (altitudes ranging from 15-120m), CCTV, and wearable cameras. This dataset offers a real-world benchmark for evaluating the robustness to significant viewpoint changes, scale variations, and resolution differences in cross-platform aerial-ground settings. In addition, to address these challenges, we propose AG-VPReID-Net, an end-to-end framework composed of three complementary streams: (1) an Adapted Temporal-Spatial Stream addressing motion pattern inconsistencies and facilitating temporal feature learning, (2) a Normalized Appearance Stream leveraging physics-informed techniques to tackle resolution and appearance changes, and (3) a Multi-Scale Attention Stream handling scale variations across drone altitudes. We integrate visual-semantic cues from all streams to form a robust, viewpoint-invariant whole-body representation. Extensive experiments demonstrate that AG-VPReID-Net outperforms state-of-the-art approaches on both our new dataset and existing video-based ReID benchmarks, showcasing its effectiveness and generalizability. Nevertheless, the performance gap observed on AG-VPReID across all methods underscores the dataset's challenging nature. The dataset, code and trained models are available at https://github.com/agvpreid25/AG-VPReID-Net.