Identifying Re-identification Challenges: Past, Current and Future Trends

• Published in: SN computer science Vol. 5; no. 7; p. 937
• Main Authors: Qian, Yan, Barthelemy, J., Karuppiah, E., Perez, P.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 07.10.2024; Springer Nature B.V
• Subjects: Cameras; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Computer vision; Data Structures and Information Theory; Datasets; Identification; Information Systems and Communication Service; Pattern Recognition and Graphics; Smart cities; Software Engineering/Programming and Operating Systems; Survey Article; Trends; Vehicles; Vision; Open-world re-identification; Closed-world re-identification; Open-set re-identification; Person re-identification; Vehicle re-identification
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-024-03271-9

Person and vehicle re-identification has been a popular subject in the field of the computer vision technologies. Existing closed-set re-identification surpasses human-level accuracies on commonly used benchmarks, and the research focus for re-identification is shifting to the open world-setting. The latter setting is more suitable for practical applications, however, is less developed due to its challenges. On the other hand, existing research is more focused on person re-identification, even though both, person and vehicle, are important components for smart city applications. This review attempts to combine for the first time the problem of person and vehicle re-identification under closed and open settings, its challenges, and the existing research. Specifically, we start from the origin of the re-identification task and then summarize state-of-the-art research based on deep learning in different scenarios: person or vehicle or unified re-identification in closed- and open-world settings. Additionally, we analyse a new method for solving the re-identification task using the Transformer, a model architecture that relies entirely on an attention mechanism, which shows promising results. This survey facilitates future research by providing a summary on past and present trends, and aids to improve the usability of re-ID techniques.