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One-Shot Person Re-identification with a Consumer Depth Camera

  • Matteo MunaroEmail author
  • Andrea Fossati
  • Alberto Basso
  • Emanuele Menegatti
  • Luc Van Gool
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

In this chapter, we propose a comparison between two techniques for one-shot person re-identification from soft biometric cues. One is based upon a descriptor composed of features provided by a skeleton estimation algorithm; the other compares body shapes in terms of whole point clouds. This second approach relies on a novel technique we propose to warp the subject’s point cloud to a standard pose, which allows to disregard the problem of the different poses a person can assume. This technique is also used for composing 3D models which are then used at testing time for matching unseen point clouds. We test the proposed approaches on an existing RGB-D re-identification dataset and on the newly built BIWI RGBD-ID dataset. This dataset provides sequences of RGB, depth, and skeleton data for 50 people in two different scenarios and it has been made publicly available to foster advancement in this new research branch.

Notes

Acknowledgments

The authors would like to thank all the people at the BIWI laboratory of ETH Zurich who took part in the BIWI RGBD-ID dataset.

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Matteo Munaro
    • 1
    Email author
  • Andrea Fossati
    • 2
  • Alberto Basso
    • 1
  • Emanuele Menegatti
    • 1
  • Luc Van Gool
    • 2
  1. 1.Intelligent Autonomous Systems LaboratoryUniversity of PaduaPaduaItaly
  2. 2.Computer Vision LaboratoryETH ZurichZurichSwitzerland

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