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020 _a9783031018251
_9978-3-031-01825-1
024 7 _a10.1007/978-3-031-01825-1
_2doi
050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYT
_2thema
082 0 4 _a006
_223
100 1 _aPanda, Rameswar.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980205
245 1 0 _aPerson Re-Identification with Limited Supervision
_h[electronic resource] /
_cby Rameswar Panda, Amit K. Roy-Chowdhury.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXI, 86 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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 withlimited 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.
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_980206
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aComputer Vision.
_980207
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aRoy-Chowdhury, Amit K.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980208
710 2 _aSpringerLink (Online service)
_980209
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000829
776 0 8 _iPrinted edition:
_z9783031006975
776 0 8 _iPrinted edition:
_z9783031029530
830 0 _aSynthesis Lectures on Computer Vision,
_x2153-1064
_980210
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01825-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c84917
_d84917