Smart City Gnosys
Smart city article details
| Title | Identifying Re-Identification Challenges: Past, Current And Future Trends |
|---|---|
| ID_Doc | 30088 |
| Authors | Qian Y.; Barthelemy J.; Karuppiah E.; Perez P. |
| Year | 2024 |
| Published | SN Computer Science, 5, 7 |
| DOI | http://dx.doi.org/10.1007/s42979-024-03271-9 |
| Abstract | 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. © The Author(s) 2024. |
| Author Keywords | Closed-world re-identification; Open-set re-identification; Open-world re-identification; Person re-identification; Vehicle re-identification |
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