  Video-based person re-identification deals with the inherent difficulty of
matching unregulated sequences with different length and with incomplete target
pose/viewpoint structure. Common approaches operate either by reducing the
problem to the still images case, facing a significant information loss, or by
exploiting inter-sequence temporal dependencies as in Siamese Recurrent Neural
Networks or in gait analysis. However, in all cases, the inter-sequences
pose/viewpoint misalignment is not considered, and the existing spatial
approaches are mostly limited to the still images context. To this end, we
propose a novel approach that can exploit more effectively the rich video
information, by accounting for the role that the changing pose/viewpoint factor
plays in the sequences matching process. Specifically, our approach consists of
two components. The first one attempts to complement the original
pose-incomplete information carried by the sequences with synthetic
GAN-generated images, and fuse their feature vectors into a more discriminative
viewpoint-insensitive embedding, namely Weighted Fusion (WF). Another one
performs an explicit pose-based alignment of sequence pairs to promote coherent
feature matching, namely Weighted-Pose Regulation (WPR). Extensive experiments
on two large video-based benchmark datasets show that our approach outperforms
considerably existing methods.
