  Although the performance of person re-identification (Re-ID) has been much
improved by using sophisticated training methods and large-scale labelled
datasets, many existing methods make the impractical assumption that
information of a target domain can be utilized during training. In practice, a
Re-ID system often starts running as soon as it is deployed, hence training
with data from a target domain is unrealistic. To make Re-ID systems more
practical, methods have been proposed that achieve high performance without
information of a target domain. However, they need cumbersome tuning for
training and unusual operations for testing. In this paper, we propose
augmented hard example mining, which can be easily integrated to a common Re-ID
training process and can utilize sophisticated models without any network
modification. The method discovers hard examples on the basis of classification
probabilities, and to make the examples harder, various types of augmentation
are applied to the examples. Among those examples, excessively augmented ones
are eliminated by a classification based selection process. Extensive analysis
shows that our method successfully selects effective examples and achieves
state-of-the-art performance on publicly available benchmark datasets.
