Domain-Robust Deep Hashing: A Unified Framework for Fast Person Re-Identification

Person re-identification (ReID) in large-scale surveillance requires methods that are both accurate and efficient. While deep hashing enables compact binary representations, it often suffers from accuracy degradation due to the domain gap between raw features and hash codes. This paper proposes a unified, open-source framework for fast person ReID that introduces a cross-domain loss function to explicitly bridge the feature and hash spaces. Our model-agnostic training strategy integrates seamlessly with existing architectures such as ResNet and OSNet. Experiments on Market1501 and CUHK03 demonstrate that the proposed framework outperforms state-of-the-art deep hashing and fast ReID methods, achieving up to 8.61% higher mean Average Precision (mAP). Extensive ablation studies validate the contribution of the cross-domain loss, and evaluations across multiple backbones confirm the framework's versatility. The results show that our approach not only improves accuracy but also provides a strong, reproducible baseline for efficient person re-identification.