  Existing person re-identification (re-id) methods rely mostly on a large set
of inter-camera identity labelled training data, requiring a tedious data
collection and annotation process therefore leading to poor scalability in
practical re-id applications. To overcome this fundamental limitation, we
consider person re-identification without inter-camera identity association but
only with identity labels independently annotated within each individual
camera-view. This eliminates the most time-consuming and tedious inter-camera
identity labelling process in order to significantly reduce the amount of human
efforts required during annotation. It hence gives rise to a more scalable and
more feasible learning scenario, which we call Intra-Camera Supervised (ICS)
person re-id. Under this ICS setting with weaker label supervision, we
formulate a Multi-Task Multi-Label (MTML) deep learning method. Given no
inter-camera association, MTML is specially designed for self-discovering the
inter-camera identity correspondence. This is achieved by inter-camera
multi-label learning under a joint multi-task inference framework. In addition,
MTML can also efficiently learn the discriminative re-id feature
representations by fully using the available identity labels within each
camera-view. Extensive experiments demonstrate the performance superiority of
our MTML model over the state-of-the-art alternative methods on three
large-scale person re-id datasets in the proposed intra-camera supervised
learning setting.
