Person Re Identification by Camera Correlation Aware Feature Augmentation
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 coR relation 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. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state of the art competitors on contemporary challenging person re id datasets.
IEEE Transactions on Pattern Analysis and Machine Intelligence