عنوان انگلیسی مقاله:
Deep learning-based methods for person re-identification: A comprehensive review
ترجمه فارسی عنوان مقاله:
روشهای مبتنی بر یادگیری عمیق برای شناسایی مجدد شخص: مرور جامع
Sciencedirect - Elsevier - Neurocomputing, 337 (2019) 354-371: doi:10:1016/j:neucom:2019:01:079
Di Wu a , Si-Jia Zheng a , Xiao-Ping Zhang a , Chang-An Yuan b , Fei Cheng c , Yang Zhao c , Yong-Jun Lin c , Zhong-Qiu Zhao d , Yong-Li Jiang e , De-Shuang Huang
In recent years, person re-identification (ReID) has received much attention since it is a fundamental task in intelligent surveillance systems and has widespread application prospects in numerous fields. Given an image of a pedestrian captured from one camera, the task is to identify this pedestrian from the gallery set captured by other multiple cameras. It is a challenging issue since the appearance of a pedestrian may suffer great changes across different cameras. The task has been greatly boosted by deep learn- ing technology. There are mainly six types of deep learning-based methods designed for this issue, i.e. identification deep model, verification deep model, distance metric-based deep model, part-based deep model, video-based deep model and data augmentation-based deep model. In this paper, we first give a comprehensive review of current six types of deep learning methods. Second, we present the detailed descriptions of existing person ReID datasets. Then, some state-of-the-art performances of methods over recent years on several representative ReID datasets are summarized. Finally, we conclude this paper and discuss the future directions of the person ReID.
Keywords: Person re-identification | Deep learning | Literature review