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

Shangdong Zhu () and Huayan Zhang
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Shangdong Zhu: School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Huayan Zhang: School of Computer Science, Liaocheng University, Liaocheng 252000, China

Mathematics, 2025, vol. 13, issue 16, 1-17

Abstract: 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.

Keywords: person re-identification; diagonal swapping augmentation strategy; image feature-level erasing; data augmentation; computer vision (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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