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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.
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
Slawomir Bak 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
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