Abstract:

In order to detect the spatio-temporal interest points that illustrate the characteristics of human action and possess robustness to noise and camera zooming,first,a novel detector for spatio-temporal interest points is proposed. Next,by centering on the detected spatio-temporal interest point,a polyhedron model-based spatio-temporal gradient descriptor is created to illustrate the spatio-temporal visual features of human action. Then,a larger and more efficient codebook of video action clips is constructed by using the Bag of Words method based on the hierarchical vocabulary tree. Finally,by integrating the descriptor with the high-level action attributes defined by human,the latent support vector machine combined with coordinate descent is adopted to find the local optimum of the prediction model. Experiments on some typical databases demonstrate that the proposed method achieves high recognition rate of human action.

Key words: action recognition, spatio-temporal interest point, spatio-temporal gradient, Bag of Words