  The challenge of person re-identification (re-id) is to match individual
images of the same person captured by different non-overlapping camera views
against significant and unknown cross-view feature distortion. While a large
number of distance metric/subspace learning models have been developed for
re-id, the cross-view transformations they learned are view-generic and thus
potentially less effective in quantifying the feature distortion inherent to
each camera view. Learning view-specific feature transformations for re-id
(i.e., view-specific re-id), an under-studied approach, becomes an alternative
resort for this problem. In this work, we formulate a novel view-specific
person re-identification framework from the feature augmentation point of view,
called Camera coRrelation Aware Feature augmenTation (CRAFT). Specifically,
CRAFT performs cross-view adaptation by automatically measuring camera
correlation from cross-view visual data distribution and adaptively conducting
feature augmentation to transform the original features into a new adaptive
space. Through our augmentation framework, view-generic learning algorithms can
be readily generalized to learn and optimize view-specific sub-models whilst
simultaneously modelling view-generic discrimination information. Therefore,
our framework not only inherits the strength of view-generic model learning but
also provides an effective way to take into account view specific
characteristics. Our CRAFT framework can be extended to jointly learn
view-specific feature transformations for person re-id across a large network
with more than two cameras, a largely under-investigated but realistic re-id
setting. Additionally, we present a domain-generic deep person appearance
representation which is designed particularly to be towards view invariant for
facilitating cross-view adaptation by CRAFT.
