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

Improving Deep Models of Person Re-identification for Cross-Dataset Usage