Generating Domain and Pose Variations between Pair of Cameras for Person Re-Identification

Abstract

Person re-identification (re-id) remains an important task that aims to retrieve a person's images from an image dataset, given a probe image. The lack of cross-view (pose variations) training data and significant intra-class (domain) variations across different cameras make re-id more challenging. To solve these issues, this work proposes a Domain and Pose Invariant Generative Adversarial Network (DPI-GAN) to generate images for both domain and pose variations capture. It is based on a CycleGAN structure in which the generator networks are conditioned on a new pose. Identity and pose discriminators networks are used to monitor the image generation process. These generated images are used for learning domain and pose invariant features to improve the performance of person re-identification.