Data-augmentation for reducing dataset bias in person re-identification
Niall McLaughlin, Jesús Martínez del Rincón, Paul C. Miller · 2015
In this paper we explore ways to address the issue of dataset bias in person re-identification by using data augmentation to increase the variability of the available datasets, and we introduce a novel data augmentation method for re-identification based on changing the image background. We show that use of data augmentation can improve the cross-dataset generalisation of convolutional network based re-identification systems, and that changing the image background yields further improvements.