  Holistic person re-identification (ReID) has received extensive study in the
past few years and achieves impressive progress. However, persons are often
occluded by obstacles or other persons in practical scenarios, which makes
partial person re-identification non-trivial. In this paper, we propose a
spatial-channel parallelism network (SCPNet) in which each channel in the ReID
feature pays attention to a given spatial part of the body. The spatial-channel
corresponding relationship supervises the network to learn discriminative
feature for both holistic and partial person re-identification. The single
model trained on four holistic ReID datasets achieves competitive accuracy on
these four datasets, as well as outperforms the state-of-the-art methods on two
partial ReID datasets without training.
