Person reidentification by kernel PCA based appearance learning

  • Yang J
  • Shi Z
  • Vela P
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Person reidentification is a desirable feature in intelligent visual surveillance systems. This paper presents a novel person reidentification algorithm using an eigenspace appearance representation. Color and spatial information per each pixel form the pixel-wise appearance representation. Assuming a person's appearance under different poses, illumination condition and view points resides in a high dimensional, non-linear manifold, Kernel PCA is applied to represent the manifold. The similarity measurement is taken by projecting the testing data to the eigenspace representation. For efficient appearance representation, a key frame selection method is presented to select multiple representative templates for each person. Experimental results on two publicly available datasets demonstrate the performance of the proposed method.

Author-supplied keywords

  • Kernel PCA
  • Manifold learning
  • Person reidentification

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  • Jun Yang

  • Zhongke Shi

  • Patricio A. Vela

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