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Person Re-Identification

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Cover of 'Person Re-Identification'

Table of Contents

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    Book Overview
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    Chapter 1 The Re-identification Challenge
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    Chapter 2 Discriminative Image Descriptors for Person Re-identification
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    Chapter 3 SDALF: Modeling Human Appearance with Symmetry-Driven Accumulation of Local Features
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    Chapter 4 Re-identification by Covariance Descriptors
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    Chapter 5 Attributes-Based Re-identification
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    Chapter 6 Person Re-identification by Attribute-Assisted Clothes Appearance
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    Chapter 7 Person Re-identification by Articulated Appearance Matching
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    Chapter 8 One-Shot Person Re-identification with a Consumer Depth Camera
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    Chapter 9 Group Association: Assisting Re-identification by Visual Context
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    Chapter 10 Evaluating Feature Importance for Re-identification
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    Chapter 11 Learning Appearance Transfer for Person Re-identification
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    Chapter 12 Mahalanobis Distance Learning for Person Re-identification
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    Chapter 13 Dictionary-Based Domain Adaptation Methods for the Re-identification of Faces
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    Chapter 14 From Re-identification to Identity Inference: Labeling Consistency by Local Similarity Constraints
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    Chapter 15 Re-identification for Improved People Tracking
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    Chapter 16 Benchmarking for Person Re-identification
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    Chapter 17 Person Re-identification: System Design and Evaluation Overview
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    Chapter 18 People Search with Textual Queries About Clothing Appearance Attributes
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    Chapter 19 Large-Scale Camera Topology Mapping: Application to Re-identification
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    Chapter 20 Scalable Multi-camera Tracking in a Metropolis
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Person Re-Identification
Published by
Springer London, January 2014
DOI 10.1007/978-1-4471-6296-4
978-1-4471-6295-7, 978-1-4471-6296-4, 978-1-4471-7063-1

Gong, Shaogang, Loy, Chen Change, Yan, Shuicheng, Cristani, Marco

Mendeley readers

The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
Unknown 98 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 35 35%
Student > Master 21 21%
Student > Doctoral Student 9 9%
Researcher 8 8%
Student > Postgraduate 7 7%
Other 9 9%
Unknown 11 11%
Readers by discipline Count As %
Computer Science 67 67%
Engineering 16 16%
Earth and Planetary Sciences 1 1%
Business, Management and Accounting 1 1%
Unknown 15 15%