Joint Angles Similarities and HOG2 for Action Recognition

@article{OhnBar2013JointAS,
  title={Joint Angles Similarities and HOG2 for Action Recognition},
  author={Eshed Ohn-Bar and Mohan Manubhai Trivedi},
  journal={2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year={2013},
  pages={465-470}
}
We propose a set of features derived from skeleton tracking of the human body and depth maps for the purpose of action recognition. The descriptors proposed are easy to implement, produce relatively small-sized feature sets, and the multi-class classification scheme is fast and suitable for real-time applications. We intuitively characterize actions using pairwise affinities between view-invariant joint angles features over the performance of an action. Additionally, a new descriptor for spatio… CONTINUE READING
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