Person re-identification (ReID) is now an active research topic for AI-based
video surveillance applications such as specific person search, but the
practical issue that the target person(s) may change clothes (clothes
inconsistency problem) has been overlooked for long. For the first time, this
paper systematically studies this problem. We first overcome the difficulty of
lack of suitable dataset, by collecting a small yet representative real dataset
for testing whilst building a large realistic synthetic dataset for training
and deeper studies. Facilitated by our new datasets, we are able to conduct
various interesting new experiments for studying the influence of clothes
inconsistency. We find that changing clothes makes ReID a much harder problem
in the sense of bringing difficulties to learning effective representations and
also challenges the generalization ability of previous ReID models to identify
persons with unseen (new) clothes. Representative existing ReID models are
adopted to show informative results on such a challenging setting, and we also
provide some preliminary efforts on improving the robustness of existing models
on handling the clothes inconsistency issue in the data. We believe that this
study can be inspiring and helpful for encouraging more researches in this
direction. The dataset is available on the project website:
this https URL

本研究首次系统地研究了目标人物更换服装（服装不一致问题）对基于人员再识别（ReID）的 AI 视频监控应用的影响，通过采集小样本真实数据集进行测试，并构建大规模逼真的合成数据集进行训练和深入研究， 发现更换服装使得 ReID 成为一个更加困难的问题，并挑战了现有模型对于识别穿着未知（新）服装的人员的泛化能力，本研究可有助于鼓励更多相关研究。