Despite their attractive properties of invariance, robustness and reliability, statistical motion
descriptions from temporal templates have not apparently received the amount of attention they
might deserve in the human action recognition literature. In this paper, we propose an innovative
approach for action recognition, where a novel fuzzy representation based on temporal motion
templates is developed to model human actions as time series of low-dimensional descriptors. An
NB (Na¨ıve Bayes) classifier is trained on these features for action classification. When tested on
a realistic action dataset incorporating a large collection of video data, the results demonstrate
that the approach is able to achieve a recognition rate of as high as 93.7%, while remaining
tractable for real-time operation.