Group Re-Identification via Transferred Representation and Adaptive Fusion

Group re-identification (G-ReID) is a less-studied task. Its challenges include not only appearance changes of individuals which have been well-investigated in general person re-identification (ReID), but also group layout changes and group membership changes which are newly introduced by G-ReID. The key task of G-ReID is to learn representations robust to these changes. To address this issue, we design a Transferred Single and Couple Representation Learning Network (TSCN). The merits are two aspects: 1) Due to the lack of training samples, existing methods exploit unsatisfactory hand-crafted features. To obtain the superiority of deep learning models, we treat a group as multiple persons and transfer the labeled ReID dataset to the G-ReID dataset style to learn the single representation. 2) Taking into account neighborhood relationship in a group, we also propose the couple representation, which maintains more discriminative features in some cases. We also exploit an unsupervised weight learning method to adaptively fuse the results of different views together according to the result pattern. Extensive experimental results demonstrate the effectiveness of our approach, outperforming the state-of-the-art methods on two public datasets.