Virtual Try-on-Based Data Augmentation for Robust Person Re-Identification in Emergency Surveillance Scenarios

Person Re-identification (Re-ID) plays an important role in dynamic evacuation path planning and safety monitoring. However, rapid appearance changes and limited long-term surveillance data significantly degrade model robustness in emergency scenarios. To address this issue, a virtual try-on-based data augmentation framework is proposed for person Re-ID. A prompt-based automatic clothing mask generation (PACMG) module integrating Grounding DINO and the Segment Anything Model (SAM) is developed to improve clothing mask accuracy under low-resolution, occlusion, and complex background conditions. A tiered augmentation strategy is further designed to alleviate identity-level imbalance. Experimental results demonstrate that the proposed method increases the clothing replacement validity rate from 52% to 73.61% while preserving identity consistency and distribution stability, as verified through multi-level analyses. When the augmented data are incorporated into the training set, consistent improvements in Rank-1 accuracy and mAP are observed on a ResNet-50-based person Re-ID benchmark. These results indicate that the augmented data enhance robustness to appearance variation, providing practical support for robust person tracking in evacuation scenarios.