RE-IDVIS: Person Re-Identification System based on Interactive Visualization

Wang Xia, Guodao Sun, Zihao Zhu, Pan Liang, Sujia Zhu, Yiming Wu, Haoran Liang, Ronghua Liang

Abstract: Person re-identification (Re-ID) is a crucial technique in security systems. It enables the accurate identification and tracking of specific individuals from multiple camera perspectives. Despite significant progress in existing person Re-ID methods, they often struggle to achieve expected results in person retrieval in real-world application scenarios. One of the main challenges is that these models usually require manual adjustments to adapt to specific cases, and users often find it difficult to communicate their insights and requirements to the models. Additionally, model results include thousands of candidate images, making it challenging for users to browse and retrieve them efficiently. To address these issues, we propose a visual analysis system, RE-IDVIS, to bridge the communication gap between humans and machine learning models. This system integrates an unsupervised person Re-ID model, a semi-supervised human-in-the-loop re-ranking algorithm, and a series of visual designs and interactions to guide users in real-world person retrieval.