Person re-identification is the problem of associating observations of targets in different
non-overlapping cameras. Most of the existing learning-based methods have resulted in improved
performance on standard re-identification benchmarks but at the cost of time-consuming and
tediously labeled data. Motivated by this learning person re-identification models with
limited to no supervision has drawn a great deal of attention in recent years. In this book we
provide an overview of some of the literature in person re-identification and then move on to
focus on some specific problems in the context of person re-identification with limited
supervision in multi-camera environments. We expect this to lead to interesting problems for
researchers to consider in the future beyond the conventional fully supervised setup that has
been the framework for a lot of work in person re-identification. Chapter 1 starts with an
overview of the problems in person re-identification and the major research directions. We
provide an overview of the prior works that align most closely with the limited supervision
theme of this book. Chapter 2 demonstrates how global camera network constraints in the form of
consistency can be utilized for improving the accuracy of camera pair-wise person
re-identification models and also selecting a minimal subset of image pairs for labeling
without compromising accuracy. Chapter 3 presents two methods that hold the potential for
developing highly scalable systems for video person re-identification with limited supervision.
In the one-shot setting where only one tracklet per identity is labeled the objective is to
utilize this small labeled set along with a larger unlabeled set of tracklets to obtain a
re-identification model. Another setting is completely unsupervised without requiring any
identity labels. The temporal consistency in the videos allows us to infer about matching
objects across the cameras with higher confidence even with limited to no supervision. Chapter
4 investigates person re-identification in dynamic camera networks. Specifically we consider a
novel problem that has received very little attention in the community but is critically
important for many applications where a new camera is added to an existing group observing a
set of targets. We propose two possible solutions for on-boarding new camera(s) dynamically to
an existing network using transfer learning with limited additional supervision. Finally
Chapter 5 concludes the book by highlighting the major directions for future research.
