Conference proceeding
AG-VPReID: A Challenging Large-Scale Benchmark for Aerial-Ground Video-based Person Re-Identification
2025 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR, pp 1241-1251
01 Jan 2025
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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 datasets challenging nature. The dataset, code and trained models are available at AG-VPReID-Net.
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Details
- Title
- AG-VPReID: A Challenging Large-Scale Benchmark for Aerial-Ground Video-based Person Re-Identification
- Creators
- Huy Nguyen - Queensland University of TechnologyKien Nguyen - Queensland University of TechnologyAkila Pemasiri - Queensland University of TechnologyFeng Liu - Drexel University, Computer ScienceSridha Sridharan - Queensland University of TechnologyClinton Fookes - Queensland University of Technology
- Publication Details
- 2025 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR, pp 1241-1251
- Series
- IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Number of pages
- 11
- Grant note
- QUT Postgraduate Research Award DP200101942 / Australian Research Council (ARC) Discovery Project; Australian Research Council
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:001562507801060
- Scopus ID
- 2-s2.0-105017038330
- Other Identifier
- 991022197298104721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods