Foreground-aware Transformer Network for Person Re-identification

Abstract

Abstract Transformer-based person re-identification (person ReID) technologies tend to capture global information, but only focus on global features and ignore the interference of irrelevant information. To the best of our knowledge, most foreground information corresponds to pedestrian(in the person ReID datasets), enhancing foreground information or weakening background information helps to distinguish the person from the background. From this insight, we proposed a foreground-aware transformer network to achieve the task of person ReID. To make the most of foreground information for person identification, we isolate the foreground by minimizing the impact of background interference and introduce a foreground-aware loss function. This loss function directs the attention of networks toward the primary foreground information in the image, optimizing its ability to identify pedestrian. To prove the effectiveness of our proposed foreground-aware transformer network, we conducted experiments on Market1501 and MSMT17 datasets. Our experimental results indicate that the proposed method can yield substantial improvements in person ReID accuracy, demonstrating the practical value of our foreground-aware transformer network in addressing real-world person ReID challenges.