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As the technological advances of the last decade have led to increased performance and availability of video cameras, along with the rise of deep learning-based image recognition, the task of person re-identification has almost exclusively been studied on datasets with ground-based, static camera settings. Yet re-identification applications on aerial-based data captured by Unmanned Aerial Vehicles (UAVs) can be particularly valuable for monitoring public events, border protection, and law enforcement. For a long time no publicly available UAV-based re-identification datasets of sufficient size for modern machine learning techniques existed, which prevented research in this area. Recently, however, two new large-scale UAV-based datasets have been released. We examine re-identification performances of common neural networks on the newly released PRAI-1581 and P-DESTRE aerial-based datasets for UAV-related error sources and data augmentation strategies to increase robustness against them. Our findings of common error sources for these UAV-based datasets include occlusions, camera angles, bad poses, and low resolutions. Furthermore, data augmentation techniques such as rotating images during training prove to be a promising aid for training on the UAV-based data with varying camera angles. By carefully selecting robust networks in addition to choosing adequate training parameters and data augmentation strategies we are able to surpass the original re-identification accuracies published by the authors of the PRAI-1581 and the P-DESTRE dataset respectively.
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