An Introduction to Person Re-identification with Generative Adversarial Networks
It provides a survey for researchers in computer vision, but is incremental as it reviews existing work rather than presenting new results.
This paper reviews Generative Adversarial Network (GAN) based methods for person re-identification, discussing their frameworks and comparing their advantages and disadvantages to address limitations like occlusion and pose variation in traditional approaches.
Person re-identification is a basic subject in the field of computer vision. The traditional methods have several limitations in solving the problems of person illumination like occlusion, pose variation and feature variation under complex background. Fortunately, deep learning paradigm opens new ways of the person re-identification research and becomes a hot spot in this field. Generative Adversarial Nets (GANs) in the past few years attracted lots of attention in solving these problems. This paper reviews the GAN based methods for person re-identification focuses on the related papers about different GAN based frameworks and discusses their advantages and disadvantages. Finally, it proposes the direction of future research, especially the prospect of person re-identification methods based on GANs.