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Books > Computing & IT > Applications of computing > Image processing
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Person Re-Identification with Limited Supervision (Paperback)
Loot Price: R821
Discovery Miles 8 210
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Person Re-Identification with Limited Supervision (Paperback)
Series: Synthesis Lectures on Computer Vision
Expected to ship within 10 - 15 working days
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Total price: R841
Discovery Miles: 8 410
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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.
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