A Novel Framework for Data Augmentation on Appearance Changes for Long-Term Person Re-Identification

The general Re-ID works including datasets and networks have an assumption that the pedestrians do not change their appearances throughout the research. The lack of diversity about the appearance of the Re-ID datasets significantly limits the performance of models in cross-appearance Re-ID. Therefore, this paper conduct data augmentation in the appearance dimension to the datasets by the generation framework CAG(cross-appearance images generation) to support the studies about cross-appearance Re-ID. To demonstrate the effectiveness of the generation framework, we selected several classical and SOTA Re-ID models and conducted experiments on three commonly used cross-appearance Re-ID datasets, NKUP+IPRCC/DeepChange. The results show that the cross-appearance Re-ID images generated by CAG can help the models to obtain the robust pedestrian feature, and the diversity of appearance is a universal method for cross-appearance person re-identification.