Person Re-identification Using Partial Least Squares Appearance Modeling

  • Gabriel Lorencetti Prado
  • William Robson Schwartz
  • Helio Pedrini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)


Due to the large areas covered by surveillance systems, employed cameras usually lack intersection of field of view, refraining us from mapping the location of a person in a camera to another one. Therefore, when a subject appears in a camera, a person re-identification method is required to discover whether the subject has been previously identified in a different camera. Even though several approaches have been proposed in the literature, person re-identification is still a challenging problem due to appearance variation between cameras, changes in illumination, pose variation, and low quality data, among others. To reduce the effect of the aforementioned difficulties, we propose a person re-identification approach that models the appearance of the subjects based on multiple samples collected from multiple cameras and employs person detection and tracking to enhance the robustness of the method. Experiments conducted on three public available data sets demonstrate improvements over existing methods.


person re-identification partial least squares appearance-based modeling person detection object tracking 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gabriel Lorencetti Prado
    • 1
  • William Robson Schwartz
    • 2
  • Helio Pedrini
    • 1
  1. 1.Institute of ComputingUniversity of CampinasCampinasBrazil
  2. 2.Department of Computer ScienceUniversidade Federal de Minas GeraisBelo HorizonteBrazil

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