Action Recognition and Prediction of Human Skeleton Based on BSCPs-RF

For the continuous action recognition of human skeleton sequence, an action recognition and prediction method based on B-spline control points-random forest (BSCPs-RF) is proposed. Firstly, the local linear regression and the single frame skeleton normalization method are used to preprocess skeleton sequence to eliminate the impacts from jitter noise, displacement and scale. Then the B-spline curve control points are used as the speed-independent feature of skeleton sequences, and the real-time behaviour sequences are labelled by adopting the synchronous voice cue words to improve the efficiency of sample collection. Finally, the method of action recognition and prediction based on random forest is employed as classifier, and an ensemble learning technology is used to optimize the multiple classifiers combination to boost the recognition accuracy. The influence of different parameter values on the recognition is analyzed. The method is tested on the MSR-Action3D test database and the real-time skeletal database collected by the RGB-D device respectively. The results show that the proposed method obtains better results than some of the existing methods on MSR-Action3D database and implements high accuracy recognition in real-time data test, which verifies the effectiveness of the proposed method.