Enhancing Long-Term Re-Identification Robustness Using Synthetic Data: A Comparative Analysis

2024-12-18

International Conference on Machine Learning and Applications

AI Summary

This paper addresses the challenge of degraded generalization in long-term person/vehicle re-identification (re-ID) caused by material aging and wear-induced appearance changes. To tackle this, we propose an aging-aware re-ID paradigm. Methodologically, we introduce Pallet-Block-2696—the first open-source aging re-ID dataset—and design a progressive aging modeling framework that integrates synthetic aging data generation with dynamic gallery updating, augmented by cross-domain generalization training. Our core contribution lies in explicitly modeling aging as a continuous evolutionary process and leveraging synthetic data to enhance model robustness against temporal appearance degradation. Experiments demonstrate that our approach improves Rank-1 accuracy by 24% under dynamic gallery settings and achieves a 13% gain using only 10% synthetic data, significantly strengthening long-term identification performance on aged samples.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Computer Vision: Motion & Tracking
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Improving re-identification robustness using synthetic data
• Predicting material wear and aging for re-identification
• Enhancing accuracy with synthetic data and gallery updates

Innovation

• Uses synthetic data to boost re-identification accuracy
• Implements continuously updating gallery for aging subjects
• Introduces open-source dataset for synthetic aging research