  Solving Single-Shot Person Re-Identification (Re-Id) by training Deep
Convolutional Neural Networks is a daunting challenge, due to the lack of
training data, since only two images per person are available. This causes the
overfitting of the models, leading to degenerated performance. This paper
formulates the Triplet Permutation method to generate multiple training sets,
from a certain re-id dataset. This is a novel strategy for feeding triplet
networks, which reduces the overfitting of the Single-Shot Re-Id model. The
improved performance has been demonstrated over one of the most challenging
Re-Id datasets, PRID2011, proving the effectiveness of the method.
