Panda Rameswar, Roy-Chowdhury Amit K. Person Re-Identification with Limited Supervision 9781636392271

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Автор: Panda Rameswar, Roy-Chowdhury Amit K.
Название: Person Re-Identification with Limited Supervision
ISBN: 9781636392271
Издательство: Mare Nostrum (Eurospan)
Классификация:

Искусственный интеллект

Машинное обучение

Машинное зрение

ISBN-10: 163639227X
Обложка/Формат: Hardback
Страницы: 98
Вес: 0.39 кг.
Дата издания: 30.09.2021
Серия: Synthesis lectures on computer vision
Язык: English
Размер: 23.50 x 19.05 x 0.48 cm
Читательская аудитория: Professional and scholarly
Ключевые слова: Artificial intelligence,Computer vision,Expert systems / knowledge-based systems, COMPUTERS / Computer Vision & Pattern Recognition,COMPUTERS / Expert Systems,COMPUTERS / Intelligence (AI) & Semantics
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Поставляется из: Англии
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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.

Person Re-Identification with Limited Supervision, Panda Rameswar, Roy-Chowdhury Amit K.