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Video Sequences Association for People Re-identification across Multiple Non-overlapping Cameras

  • Dung Nghi Truong Cong
  • Catherine Achard
  • Louahdi Khoudour
  • Lounis Douadi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

This paper presents a solution of the appearance-based people re-identification problem in a surveillance system including multiple cameras with different fields of vision. We first utilize different color-based features, combined with several illuminant invariant normalizations in order to characterize the silhouettes in static frames. A graph-based approach which is capable of learning the global structure of the manifold and preserving the properties of the original data in a lower dimensional representation is then introduced to reduce the effective working space and to realize the comparison of the video sequences. The global system was tested on a real data set collected by two cameras installed on board a train. The experimental results show that the combination of color-based features, invariant normalization procedures and the graph-based approach leads to very satisfactory results.

Keywords

Dimensionality Reduction Video Sequence Multiple Camera Invariant Normalization Nonlinear Dimensionality Reduction 
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 Berlin Heidelberg 2009

Authors and Affiliations

  • Dung Nghi Truong Cong
    • 1
  • Catherine Achard
    • 2
  • Louahdi Khoudour
    • 1
  • Lounis Douadi
    • 1
  1. 1.French National Institute for Transport and Safety Research (INRETS)Villeneuve d’AscqFrance
  2. 2.Institute of Intelligent Systems and Robotics (ISIR)UPMC Univ Paris 06IVRY SUR SEINEFrance

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