Dance movement classification based on skeleton information obtained by sensors
In order to achieve effective classification of dance movement in video standardization,a dance classification method based on Kinect sensor to obtain human skeleton motion data is proposed.Cascade vectors are generated from the six core angles of dance movements in each frame of the video.Principal Component Analysis(PCA)is used to project high-dimensional vectors into low dimensional space,and Linear Discriminant Analysis(LDA)is combined to obtain feature vectors with recognition ability.An Ex-treme Learning Machine Classifier(ELMC)based on the Rectified Linear Unit(ReLU)is designed.The ReLU function is used to solve the gradient disappearance problem in the neural network design,and the fast classification processing of dance movements is realized without weight learning.The experiment results show that compared with the traditional methods,the proposed method has better classification results,and the maximum classification rate can reach 96.5％.
principal component analysislinear discriminant analysisextreme learning machinedance movementsbone joint data
万方数据
维普
