Master’s degree in Computer Science

Universidad Autónoma de Yucatán

Thesis – Generation of Images using Generative Adversarial Networks for Augmentation of Training Data in Re-identification Models

Interest in automatic people re-identification systems has significantly grown in recent years, mainly for developing surveillance and smart shops software. Due to the variability in person posture, different lighting conditions, and occluded scenarios, together with the poor quality of the images obtained by different cameras, it is currently an unsolved problem. In machine learning-based computer vision applications with reduced data sets, one possibility to improve the performance of re-identification system is through the augmentation of the set of images or videos available for training the neural models. Currently, one of the most robust ways to generate synthetic information for data augmentation, whether it is video, images or text, are the generative adversarial networks.

This project aims to use Generative Adversarial Networks (GANs) to generate synthetic images and thus increase the quantity and diversity of training data in re-identification models.