Person Re-identification in Wide Area Camera Networks

Chapter
Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 6)

Abstract

Person re-identification is defined as the problem of matching images or videos of people taken from different cameras. It is a fundamental task in wide area surveillance for multi-camera tracking and the subsequent analysis of long term activities and behaviors of people in the scene. A person’s appearance is most often used to characterize their identity and is matched across cameras to establish re-identification. In typical surveillance scenarios, people are observed from a distance, do not present similar views across cameras, and the environment is uncontrolled and varying, which makes extracting, learning, and matching a person based on their appearance a non-trivial task. Person re-identification has been an area of intense research in the past five years, nonetheless, it remains an open problem and many of its aspects and challenges are not addressed. In this chapter, we explore the problem of person re-identification in wide area camera networks, in the context of consistent tracking over multiple cameras in order to facilitate the estimation of the global trajectory of a person over the camera network. We discuss the problem details, identify the requirements of a reasonable re-identification model, and highlight the challenges. We also present a multi-parametric model, demonstrate its effectiveness for person re-identification, and discuss its contributions.

Keywords

Re-identification Appearance model Open set matching Closed set matching 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Deptartment of Computer ScienceUniversity of HoustonHoustonUSA

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