Pedestrian Re-IDentification (Re-ID) is a hot issue in the field of computer vision and mainly focuses on “how to relate to specific person captured by different cameras in different physical locations”. Traditional methods of Re-ID were mainly based on the extraction of low-level features, such as local descriptors, color histograms and human poses. In recent years, in view of the problems in traditional methods such as pedestrian occlusion and posture disalignment, pedestrian Re-ID methods based on deep learning such as region, attention mechanism, posture and Generative Adversarial Network (GAN) were proposed and the experimental results became significantly better than before. Therefore, the researches of deep learning in pedestrian Re-ID were summarized and classified, and different from the previous reviews, the pedestrian Re-ID methods were divided into four categories to discuss in this review. Firstly, the pedestrian Re-ID methods based on deep learning were summarized by following four methods region, attention, posture, and GAN. Then the performances of mAP (mean Average Precision) and Rank-1 indicators of these methods on the mainstream datasets were analyzed. The results show that the deep learning-based methods can reduce the model overfitting by enhancing the connection between local features and narrowing domain gaps. Finally, the development direction of pedestrian Re-ID method research was forecasted.