  Person re-identification (re-id) is the task of recognizing and matching
persons at different locations recorded by cameras with non-overlapping views.
One of the main challenges of re-id is the large variance in person poses and
camera angles since neither of them can be influenced by the re-id system. In
this work, an effective approach to integrate coarse camera view information as
well as fine-grained pose information into a convolutional neural network (CNN)
model for learning discriminative re-id embeddings is introduced. In most
recent work pose information is either explicitly modeled within the re-id
system or explicitly used for pre-processing, for example by pose-normalizing
person images. In contrast, the proposed approach shows that a direct use of
camera view as well as the detected body joint locations into a standard CNN
can be used to significantly improve the robustness of learned re-id
embeddings. On four challenging surveillance and video re-id datasets
significant improvements over the current state of the art have been achieved.
Furthermore, a novel reordering of the MARS dataset, called X-MARS is
introduced to allow cross-validation of models trained for single-image re-id
on tracklet data.
