Improving Person Re-Identification via Feature Erasing-Driven Data Augmentation

Person re-identification (Re-ID) has attracted considerable attention in the field of computer vision, primarily due to its critical role in video surveillance and public security applications. However, most existing Re-ID approaches rely on image-level erasing techniques, which may inadvertently remove fine-grained visual cues that are essential for accurate identification. To mitigate this limitation, we propose an effective feature erasing-based data augmentation framework that aims to explore discriminative information within individual samples and improve overall recognition performance. Specifically, we first introduce a diagonal swapping augmentation strategy to increase the diversity of the training samples. Secondly, we design a feature erasing-driven method applied to the extracted pedestrian feature to capture identity-relevant information at the feature level. Finally, extensive experiments demonstrate that our method achieves competitive performance compared to many representative approaches.