Human Action Performance using Deep Neuro-Fuzzy Recurrent Attention
  Model
  A great number of computer vision publications have focused on distinguishing
between human action recognition and classification rather than the intensity
of actions performed. Indexing the intensity which determines the performance
of human actions is a challenging task due to the uncertainty and information
deficiency that exists in the video inputs. To remedy this uncertainty, in this
paper we coupled fuzzy logic rules with the neural-based action recognition
model to rate the intensity of a human action as intense or mild. In our
approach, we used a Spatio-Temporal LSTM to generate the weights of the
fuzzy-logic model, and then demonstrate through experiments that indexing of
the action intensity is possible. We analyzed the integrated model by applying
it to videos of human actions with different action intensities and were able
to achieve an accuracy of 89.16% on our intensity indexing generated dataset.
The integrated model demonstrates the ability of a neuro-fuzzy inference module
to effectively estimate the intensity index of human actions.
