Generation and selection of synthetic data for cross-domain person re-identification

The reliance of mainstream deep learning-based person re-identification models on large-scale labeled data for training is a costly process that requires extensive collection and labeling efforts. Additionally, the existing virtual data generation methods neglect to account for the characteristics of target domain, thereby compromising the performance of cross-domain re-identification. To address these issues, this paper proposed a synthetic data generation and selection algorithm for cross-domain person re-identification. First, this algorithm utilized the foreground information of the target domain, including the color distribution of individuals’ clothing, to guide the generation of virtual 3D human models. The background information of the target domain was employed to replace the background of source domain data. This served to enhance the data quality at the pixel level, while also guiding the model to distinguish different persons based on the foreground. Finally, the proposed method employed distribution metrics such as Wasserstein Distance to measure the feature distribution distance between the source domain and target domain. This distance was used to select the source domain training subset closest to the target domain for model training. The experimental results demonstrated the superiority of this method over other existing person virtual data generation algorithms, as it can significantly improve the cross-domain generalization performance of the person re-identification model.