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A Hough Transform-Based Voting Framework for Action Recognition

A. Yao, J. Gall and L. Van Gool
IEEE Conference on Computer Vision and Pattern Recognition


We present a method to classify and localize human actions in video using a Hough transform voting framework. Random trees are trained to learn a mapping between densely-sampled feature patches and their corresponding votes in a spatio-temporal-action Hough space. The leaves of the trees form a discriminative multi-class codebook that share features between the action classes and vote for action centers in a probabilistic manner. Using low-level features such as gradients and optical flow, we demonstrate that Hough-voting can achieve state-of-the-art performance on several datasets covering a wide range of action-recognition scenarios.

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  author = {A. Yao and J. Gall and L. Van Gool},
  title = {A Hough Transform-Based Voting Framework for Action Recognition},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
  year = {2010},
  keywords = {action recognition, generalized Hough transform}