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Volume 41 Issue 6
Jun.  2015
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Journal of Beijing University of Aeronautics and Astronautics  > 2015  >  41(6): 1122-1127.
BAI Chen, SUN Junhua. Human action recognition based on locality-constrained linear coding[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(6): 1122-1127. doi: 10.13700/j.bh.1001-5965.2014.0414(in Chinese)
Citation: BAI Chen, SUN Junhua. Human action recognition based on locality-constrained linear coding[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(6): 1122-1127. doi: 10.13700/j.bh.1001-5965.2014.0414(in Chinese)
shu

Human action recognition based on locality-constrained linear coding

doi: 10.13700/j.bh.1001-5965.2014.0414

  • School of Instrumentation Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • Received Date: 10 Jul 2014
  • Publish Date: 20 Jun 2015
    Abstract
  • Large intra-class variations of action features lead to low classification accuracy of action recognition, on the other hand, current algorithms exist drawbacks in computational complexity and extension of recognizable action classes. A method based on locality-constrained linear coding (LLC) for action recognition from depth images was proposed. In order to reduce the intra-class variations and increase classification accuracy, joints' positions, velocities and acceleration features were concatenated to form local action features, then LLC was used to calculate sparse representations of local action features. Analytical solution of LLC ensures computational speed of our method is up to 760 frames per second. Dictionary is composed by sub-dictionaries learned by K-means from features of each class separately, so global optimization is avoided during extending recognizable action classes. Moreover, to avoid classifier to be over-fitting, a dimensionality reduction method based on labels of dictionary items was proposed. The proposed method was evaluated on MSR-Action3D dataset captured by depth cameras. The experimental results show that the proposed approach achieves classification accuracy of 85.7%.

     

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