Dynamic hand gesture recognition based on SURF tracking

A novel method of dynamic hand gesture recognition based on Speeded Up Robust Features (SURF) tracking is proposed. The main characteristic is that the dominant movement direction of matched SURF points in adjacent frames is used to help describing a hand trajectory without detecting and segmenting the hand region. The dynamic hand gesture is then modeled by a series of trajectory direction data streams after time warping. Accordingly, the data stream clustering method based on correlation analysis is developed to recognize a dynamic hand gesture and to speed up calculation. The proposed algorithm is tested on 26 alphabetical hand gestures and yields a satisfactory recognition rate which is 87.1% on the training set and 84.6% on the testing set.