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Kategorie szczegółowe BISAC

Person Re-Identification

ISBN-13: 9781447162957 / Angielski / Twarda / 2014 / 445 str.

Shaogang Gong; Marco Cristani; Shuicheng Yan
Person Re-Identification Shaogang Gong Marco Cristani Shuicheng Yan 9781447162957 Springer - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Person Re-Identification

ISBN-13: 9781447162957 / Angielski / Twarda / 2014 / 445 str.

Shaogang Gong; Marco Cristani; Shuicheng Yan
cena 403,47 zł
(netto: 384,26 VAT:  5%)

Najniższa cena z 30 dni: 385,52 zł
Termin realizacji zamówienia:
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Re-identification offers a useful tool for non-invasive biometric validation, surveillance, and human-robot interaction in a broad range of applications from crowd traffic management to personalised healthcare.This comprehensive volume is the first work of its kind dedicated to addressing the challenge of Person Re-Identification, presenting insights from an international selection of leading authorities in the field. Taking a strongly multidisciplinary approach, the text provides an in-depth discussion of recent developments and state-of-the-art methods drawn from the computer vision, pattern recognition and machine learning communities, embracing both fundamental research and practical applications.Topics and features: introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms, and examines the benefits of semantic attributes; describes how to segregate meaningful body parts from background clutter; examines the use of 3D depth images, and contextual constraints derived from the visual appearance of a group; reviews approaches to feature transfer function and distance metric learning, and discusses potential solutions to issues of data scalability and identity inference; investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference, and describes techniques for improving post-rank search efficiency; explores the design rationale and implementation considerations of building a practical re-identification system.This timely collection will be of great interest to academics, industrial researchers and postgraduates involved in computer vision and machine learning, database image retrieval, big data mining, and search engines, as well as to developers keen to exploit this emerging technology for commercial applications.

Kategorie:
Informatyka
Kategorie BISAC:
Computers > Software Development & Engineering - Computer Graphics
Computers > Artificial Intelligence - Computer Vision & Pattern Recognition
Computers > Data Science - General
Wydawca:
Springer
Seria wydawnicza:
Advances in Computer Vision and Pattern Recognition
Język:
Angielski
ISBN-13:
9781447162957
Rok wydania:
2014
Wydanie:
2014
Numer serii:
000418995
Ilość stron:
445
Waga:
0.80 kg
Wymiary:
23.9 x 16.3 x 2.3
Oprawa:
Twarda
Wolumenów:
01
Dodatkowe informacje:
Bibliografia
Wydanie ilustrowane

The book is intended for researchers that work, or want to start working on this research topic. ... The book is very interesting and allows having an insight into the problem of person re-identification and its potential applications. It provides a presentation of the current state-of-the-are and recent progress on this topic. I think that researchers who intend to work on re-identification can benefit from reading this book. They will be introduced to the many interesting challenges to be faced." (Donatello Conte, IAPR Newsletter, Vol. 37 (2), 2015)

The Re-Identification Challenge
Shaogang Gong, Marco Cristani, Chen Change Loy, and Timothy M. Hospedales

Part I: Features and Representations

Discriminative Image Descriptors for Person Re-Identification
Bingpeng Ma, Yu Su, and Frédéric Jurie

SDALF: Modeling Human Appearance with Symmetry-Driven Accumulation of Local Features
Loris Bazzani, Marco Cristani, and Vittorio Murino

Re-Identification by Covariance Descriptors
Sławomir Bąk and François Brémond

Attributes-Based Re-Identification
Ryan Layne, Timothy M. Hospedales, and Shaogang Gong

Person Re-Identification by Attribute-Assisted Clothes Appearance
Annan Li, Luoqi Liu, and Shuicheng Yan

Person Re-Identification by Articulated Appearance Matching
Dong Seon Cheng and Marco Cristani

One-Shot Person Re-Identification with a Consumer Depth Camera
Matteo Munaro, Andrea Fossati, Alberto Basso, Emanuele Menegatti, and Luc Van Gool

Group Association: Assisting Re-Identification by Visual Context
Wei-Shi Zheng, Shaogang Gong, and Tao Xiang

Evaluating Feature Importance for Re-Identification
Chunxiao Liu, Shaogang Gong, Chen Change Loy, and Xinggang Lin

Part II: Matching and Distance Metric

Learning Appearance Transfer for Person Re-Identification
Tamar Avraham and Michael Lindenbaum

Mahalanobis Distance Learning for Person Re-Identification
Peter M. Roth, Martin Hirzer, Martin Köstinger, Csaba Beleznai, and Horst Bischof

Dictionary-Based Domain Adaptation Methods for the Re-Identification of Faces
Qiang Qiu, Jie Ni, and Rama Chellappa

From Re-Identification to Identity Inference: Labelling Consistency by Local Similarity Constraints
Svebor Karaman, Giuseppe Lisanti, Andrew D. Bagdanov, and Alberto Del Bimbo

Re-Identification for Improved People Tracking
François Fleuret, Horesh Ben Shitrit, and Pascal Fua

Part III: Evaluation and Application

Benchmarking for Person Re-Identification
Roberto Vezzani and Rita Cucchiara

Person Re-Identification: System Design and Evaluation Overview
Xiaogang Wang and Rui Zhao

People Search with Textual Queries about Clothing Appearance Attributes
Riccardo Satta, Federico Pala, Giorgio Fumera, and Fabio Roli

Large Scale Camera Topology Mapping: Application to Re-Identification
Anthony Dick, Anton van den Hengel, and Henry Detmold

Scalable Multi-Camera Tracking in a Metropolis
Yogesh Raja and Shaogang Gong

Dr. Shaogang Gong is a Professor of Visual Computation in the School of Electronic Engineering and Computer Science at Queen Mary University of London, UK. His publications include the successful Springer books Visual Analysis of Behaviour and Video Analytics for Business Intelligence. Dr. Marco Cristani is an Assistant Professor in the Computer Science Department at the University of Verona, Italy. Dr. Shuicheng Yan is an Associate Professor in the Department of Electrical and Computer Engineering at the National University of Singapore. Dr. Chen Change Loy is a Research Assistant Professor in the Department of Information Engineering at the Chinese University of Hong Kong.

Re-identification offers a useful tool for non-invasive biometric validation, surveillance, and human-robot interaction in a broad range of applications from crowd traffic management to personalised healthcare.

This comprehensive volume is the first work of its kind dedicated to addressing the challenge of Person Re-Identification, presenting insights from an international selection of leading authorities in the field. Taking a strongly multidisciplinary approach, the text provides an in-depth discussion of recent developments and state-of-the-art methods drawn from the computer vision, pattern recognition and machine learning communities, embracing both fundamental research and practical applications.

Topics and features:

  • Introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms, and examines the benefits of semantic attributes
  • Describes how to segregate meaningful body parts from background clutter
  • Examines the use of 3D depth images, and contextual constraints derived from the visual appearance of a group
  • Reviews approaches to feature transfer function and distance metric learning, and discusses potential solutions to issues of data scalability and identity inference
  • Investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference, and describes techniques for improving post-rank search efficiency
  • Explores the design rationale and implementation considerations of building a practical re-identification system

This timely collection will be of great interest to academics, industrial researchers and postgraduates involved in computer vision and machine learning, database image retrieval, big data mining, and search engines, as well as to developers keen to exploit this emerging technology for commercial applications.



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