A vital requirement of any recognition system claiming to be real time is the capability to perform
feature extraction in real time. In this paper, we propose an innovative fuzzy approach for real-time
dynamic gesture recognition and spotting, where a compact local descriptor is designed to model
moving gesture skeletons as a time series of fuzzy statistical features. Then, a set of one-vs-rest SVMs
is trained on these features for gesture recognition and spotting. In this approach, the meaningful
hand movements are successfully spotted while concurrently removing unintentional hand movements
from an input video sequence. When evaluated on a gesture data set incorporating a relatively large
and diverse collection of video data, the method proposed yields promising results that compare very
favorably with those reported in the literature, while retaining real-time performance.