  Despite the recent success of deep learning architectures, person
re-identification (ReID) remains a challenging problem in real-word
applications. Several unsupervised single-target domain adaptation (STDA)
methods have recently been proposed to limit the decline in ReID accuracy
caused by the domain shift that typically occurs between source and target
video data. Given the multimodal nature of person ReID data (due to variations
across camera viewpoints and capture conditions), training a common CNN
backbone to address domain shifts across multiple target domains, can provide
an efficient solution for real-time ReID applications. Although multi-target
domain adaptation (MTDA) has not been widely addressed in the ReID literature,
a straightforward approach consists in blending different target datasets, and
performing STDA on the mixture to train a common CNN. However, this approach
may lead to poor generalization, especially when blending a growing number of
distinct target domains to train a smaller CNN.
  To alleviate this problem, we introduce a new MTDA method based on knowledge
distillation (KD-ReID) that is suitable for real-time person ReID applications.
Our method adapts a common lightweight student backbone CNN over the target
domains by alternatively distilling from multiple specialized teacher CNNs,
each one adapted on data from a specific target domain. Extensive experiments
conducted on several challenging person ReID datasets indicate that our
approach outperforms state-of-art methods for MTDA, including blending methods,
particularly when training a compact CNN backbone like OSNet. Results suggest
that our flexible MTDA approach can be employed to design cost-effective ReID
systems for real-time video surveillance applications.
