Structure Sparsity for Multi-camera Gait Recognition

  • Qiyue Yin
  • Rong Sun
  • Liang Wang
  • Ran He
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)


With the rapid development of surveillance technology, there are often several cameras in one scenario. The multi-camera usage to perform gait recognition becomes a challenge problem. This paper studies multi-camera gait recognition via structure sparsity. For the multi-camera structure in the training set, we propose a structure sparsity algorithm to learn informative and discriminative sparse representations; and for the structure in the testing set, we develop a new classification criteria based on the reconstruction error of learned sparse representations. In addition, we learn a dictionary from the original gait data to further improve recognition accuracy meanwhile reduce computational cost. Experimental results show that the proposed method can efficiently deal with the multi-camera gait recognition problem and outperforms the state-of-the-art sparse representation methods.


multi-camera gait recognition structure sparsity sparse representation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Qiyue Yin
    • 1
  • Rong Sun
    • 1
  • Liang Wang
    • 2
  • Ran He
    • 2
  1. 1.Harbin Engineering UniversityChina
  2. 2.Institute of AutomationChinese Academy of SciencesChina

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