  Appearance based person re-identification in a real-world video surveillance
system with non-overlapping camera views is a challenging problem for many
reasons. Current state-of-the-art methods often address the problem by relying
on supervised learning of similarity metrics or ranking functions to implicitly
model appearance transformation between cameras for each camera pair, or group,
in the system. This requires considerable human effort to annotate data.
Furthermore, the learned models are camera specific and not transferable from
one set of cameras to another. Therefore, the annotation process is required
after every network expansion or camera replacement, which strongly limits
their applicability. Alternatively, we propose a novel modeling approach to
harness complementary appearance information without supervised learning that
significantly outperforms current state-of-the-art unsupervised methods on
multiple benchmark datasets.
