This contribution explores the impact of synthetic training data usage and the prediction of material wear and aging in the context of re-identification. Different experimental setups and gallery set expanding strategies are tested, analyzing their impact on performance over time for aging re-identification subjects. Using a continuously updating gallery, we were able to increase our mean Rank-1 accuracy by 24%, as material aging was taken into account step by step. In addition, using models trained with 10% artificial training data, Rank-1 accuracy could be increased by up to 13%, in comparison to a model trained on only real-world data, significantly boosting generalized performance on hold-out data. Finally, this work introduces a novel, open-source re-identification dataset, pallet-block-2696. This dataset contains 2,696 images of Euro pallets, taken over a period of 4 months. During this time, natural aging processes occurred and some of the pallets were damaged during their usage. These wear and tear processes significantly changed the appearance of the pallets, providing a dataset that can be used to generate synthetically aged pallets or other wooden materials.
@article{arxiv.2504.18286,
title = {Enhancing Long-Term Re-Identification Robustness Using Synthetic Data: A Comparative Analysis},
author = {Christian Pionzewski and Rebecca Rademacher and Jérôme Rutinowski and Antonia Ponikarov and Stephan Matzke and Tim Chilla and Pia Schreynemackers and Alice Kirchheim},
journal= {arXiv preprint arXiv:2504.18286},
year = {2025}
}
Comments
Published in: 2024 International Conference on Machine Learning and Applications (ICMLA), IEEE. 6 pages, 3 figures