Domain Transfer Between Images with GANs

Abstract

Person re-identification (re-ID) is the problem of identifying the same person in multiple cameras. This is a non-trivial problem, that is confounded by non-overlapping field of view, lighting differences, occlusion, variation in poses and different camera viewpoints. Current person re-ID systems perform well on specific datasets but experience large performance drops when trained and tested on a different dataset.

This report describes a new method to improve the robustness of person re-ID models. The proposed method generates new backgrounds using a generative adversarial network which allows person re-ID models to be trained on larger and more varied datasets, therefore improving robustness. Individual identities from the original dataset are recreated in new scenarios with corresponding labels, this allows person re-ID networks to utilise supervised learning on the generated data.

Variations of the proposed method provide significant control over the generated images, from maintaining high similarity between the generated identities and their respective original (same pose) to generating the identity in any new pose while still maintaining significant similarities.

Keywords

Citation

@mastersthesis{kolbeinsson2019gans,
  author = {Benedikt Kolbeinsson},
  title  = {Domain Transfer Between Images with GANs},
  school = {Imperial College London},
  year   = {2019},
  type   = {MEng Thesis}
}