A Comprehensive Review on Group Re-identification in Surveillance Videos
Kaushik Nayak, Debi Prosad Dogra · ACM Computing Surveys · 2025
Computer vision plays an important role in the automated analysis of human groups. The appearance of human groups has been studied for various reasons, including detection, identification, tracking, and re-identification. Person re-identification has been studied extensively over the past decade. Despite significant efforts by the computer vision research community, person re-identification often suffers from issues such as similar clothing appearances, occlusion, and viewpoint changes. However, group re-identification has not received much attention. It involves identifying human groups across multiple non-overlapping camera views. It is a challenging problem that suffers from issues related to person re-identification and additional challenges like variations in the number of persons and the structural layout of groups. This article summarizes the research paradigms of human group analysis. It reviews the recent advancements in group re-identification, including key challenges, datasets, and state-of-the-art methods. The work concludes with a discussion of open research challenges and future directions in group re-identification, including the need for reliable techniques, varied datasets, and ethical considerations regarding privacy. Overall, this article offers a thorough and up-to-date summary of the most recent findings in group re-identification. It also identifies the research gaps as placeholders for further study.