Abstract
Person re-identification (ReID) is an important task in
the field of intelligent security. A key challenge is how
to capture human pose variations, while existing benchmarks (i.e., Market1501, DukeMTMC-reID, CUHK03, etc.)
do NOT provide sufficient pose coverage to train a robust
ReID system. To address this issue, we propose a posetransferrable person ReID framework which utilizes posetransferred sample augmentations (i.e., with ID supervision) to enhance ReID model training. On one hand, novel
training samples with rich pose variations are generated via
transferring pose instances from MARS dataset, and they
are added into the target dataset to facilitate robust training. On the other hand, in addition to the conventional discriminator of GAN (i.e., to distinguish between REAL/FAKE
samples), we propose a novel guider sub-network which encourages the generated sample (i.e., with novel pose) towards better satisfying the ReID loss (i.e., cross-entropy
ReID loss, triplet ReID loss). In the meantime, an alternative optimization procedure is proposed to train the
proposed Generator-Guider-Discriminator network. Experimental results on Market-1501, DukeMTMC-reID and
CUHK03 show that our method achieves great performance
improvement, and outperforms most state-of-the-art methods without elaborate designing the ReID model