Human Action Performance using Deep Neuro-Fuzzy Recurrent Attention Model

Nihar Bendre, Nima Ebadi, John J. Prevost, Peyman Najafirad
2020 IEEE Access  
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
more » ... e 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 fuzzylogic 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. INDEX TERMS Attention mechanism, artificial intelligence, behavior analysis, computer vision, convolutional neural networks, fuzzy logic, human action recognition, intensity indexing, machine learning, neurofuzzy systems, recurrent neural networks, supervised learning.
doi:10.1109/access.2020.2982364 fatcat:4cijvaniajfg5ht2s55by53kve