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Person Re-identification: System Design and Evaluation Overview

  • Xiaogang Wang
  • Rui Zhao
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Person re-identification has important applications in video surveillance. It is particularly challenging because observed pedestrians undergo significant variations across camera views, and there are a large number of pedestrians to be distinguished given small pedestrian images from surveillance videos. This chapter discusses different approaches of improving the key components of a person re-identification system, including feature design, feature learning, and metric learning, as well as their strength and weakness. It provides an overview of various person re-identification systems and their evaluation on benchmark datasets. Multiple benchmark datasets for person re-identification are summarized and discussed. The performance of some state-of-the-art person identification approaches on benchmark datasets is compared and analyzed. It also discusses a few future research directions on improving benchmark datasets, evaluation methodology, and system design.

Keywords

Query Image Benchmark Dataset Camera View Fisher Vector Large Margin Nearest Neighbor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Department of Electronic EngineeringThe Chinese University of Hong KongShatinHong Kong

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