A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification
This work addresses domain adaptation for person re-identification, which is incremental as it builds on existing methods by incorporating camera-level sub-domains and temporal information.
The paper tackles unsupervised cross-domain person re-identification by addressing data distribution discrepancies and lack of labels, using camera-aware domain adaptation and temporal continuity to create discriminative information, achieving superior results on benchmark datasets.
Unsupervised cross-domain person re-identification (Re-ID) faces two key issues. One is the data distribution discrepancy between source and target domains, and the other is the lack of labelling information in target domain. They are addressed in this paper from the perspective of representation learning. For the first issue, we highlight the presence of camera-level sub-domains as a unique characteristic of person Re-ID, and develop camera-aware domain adaptation to reduce the discrepancy not only between source and target domains but also across these sub-domains. For the second issue, we exploit the temporal continuity in each camera of target domain to create discriminative information. This is implemented by dynamically generating online triplets within each batch, in order to maximally take advantage of the steadily improved feature representation in training process. Together, the above two methods give rise to a novel unsupervised deep domain adaptation framework for person Re-ID. Experiments and ablation studies on benchmark datasets demonstrate its superiority and interesting properties.