  Person re-identification (Re-ID) is the task of matching humans across
cameras with non-overlapping views that has important applications in visual
surveillance. Like other computer vision tasks, this task has gained much with
the utilization of deep learning methods. However, existing solutions based on
deep learning are usually trained and tested on samples taken from same
datasets, while in practice one need to deploy Re-ID systems for new sets of
cameras for which labeled data is unavailable. Here, we mitigate this problem
for one state-of-the-art model, namely, metric embedding trained with the use
of the triplet loss function, although our results can be extended to other
models. The contribution of our work consists in developing a method of
training the model on multiple datasets, and a method for its online
practically unsupervised fine-tuning. These methods yield up to 19.1%
improvement in Rank-1 score in the cross-dataset evaluation.
