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008			220601s2021 sz | s |||| 0|eng d
020			_a9783031018251
024	7		_a10.1007/978-3-031-01825-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7882.P3 _b2021 EB
100	1		_aPanda, Rameswar _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686991
245	1	0	_aPerson Re-Identification with Limited Supervision _cby Rameswar Panda, Amit K. Roy-Chowdhury
250			_a1st edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XI, 86 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Computer Vision _x2153-1064
505	0		_aPreface -- Person Re-identification: An Overview -- Supervised Re-identification: Optimizing the Annotation Effort -- Towards Unsupervised Person Re-identification -- Re-identification in Dynamic Camera Networks -- Future Research Directions -- Bibliography -- Authors' Biographies.
520			_aPerson 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.
988			_aSynthesis Collection of Technology_2021
650		7	_2embne _9152614 _aReconocimiento de formas
650		7	_2embne _9679662 _aIdentificación de personas
650		7	_2embne _9156182 _aVídeo digital
700	1		_aRoy-Chowdhury, Amit K. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686990
776	0	8	_iPrinted edition: _z9783031000829
776	0	8	_iPrinted edition: _z9783031006975
776	0	8	_iPrinted edition: _z9783031029530
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01825-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI