Enhancing Long-Term Re-Identification Robustness Using Synthetic Data: A Comparative Analysis
This work addresses re-identification robustness for aging objects like pallets, but it is incremental as it builds on existing methods with new data and strategies.
The paper tackled the problem of long-term re-identification robustness by using synthetic data and accounting for material aging, resulting in a 24% increase in mean Rank-1 accuracy with a continuously updating gallery and up to a 13% boost with 10% artificial training data.
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.