On the development of Robust Longitudinal Person Re-Identification

作为一个新兴的研究领域，人的再识别（Re-ID）近年来受到越来越多的关注。基于所采用的模式，重新识别的研究可以大致分为三个分支：1)通过多个不重叠的摄像机检索感兴趣的人；2)基于一系列视频片段重新识别个人；3)建立基于生物信号的纵向鲁棒模板。本项目致力于深入探索人物Re-ID，并在选定的研究方向内进一步推进路线。随着深度神经网络的发展和智能视频监控需求的增加，该问题在计算机视觉领域引起了越来越多的关注。由于公共数据库的可用性，成功的候选人可能会接受训练，掌握图像和时间序列数据的争论，这为学生提供了更广阔的职业前景。与本项目相关的重要论文之一如下：b[1]叶明，沈俊，林国光，向涛，邵立，许少华，“深度学习用于人物再识别：综述与展望”。模式分析与机器智能学报，2021。

As one of the most rapidly emerging research areas, person re-identification (Re-ID) has been increasingly receiving attention in recent years. Based on the employed modality in question, the research on Re-ID could be broadly categorised into three branches: 1) retrieving a person of interest across multiple non-overlapping cameras, 2) re-identifying an individual based on a series of video clips, 3) establishing the longitudinally robust templates for biosignal-based person recognition.This project is devoted to thoroughly exploring the person Re-ID and further push the line within the selected research direction. With the advancement of deep neural networks and the increasing demand of intelligent video surveillance, the proposed question has gained significantly increased interest in the computer vision community in particular. Thanks to the availability of publicly databases, the successful candidate would potentially be trained to master both image and time-series data wrangling, which paves the way for the student with a much broader career perspective. One of the import papers related to this project is listed below [1]:[1] M. Ye, J. Shen, G. Lin, T. Xiang, L. Shao, and S. C. H. Hoi, "Deep Learning for Person Re-identification: A Survey and Outlook". IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.