Human motion sequence recognition based on correlation feature selection and multilayer perceptron
Yongjun Gao, Mei Wang, Yunlei Yu, Chen Zhang · 2020
Aiming at the problems of large dimension and low recognition efficiency of human action sequence recognition, a recognition algorithm based on correlation feature selection and multi-layer perceptron is proposed. Use Kinect to obtain continuous images of human movements. By extracting key frames of human movements and calculating human joint angles, key features of human movements are extracted. Through the correlation feature selection algorithm, the most effective feature subset is selected from a group of features to reduce the dimension of feature space and reduce the calculation amount of classification recognition. Finally, the multi-layer perceptron is used as the classifier, and the BP algorithm is used to adjust the network weights to realize the classification and recognition of human action sequences. Through the classification and recognition experiments of human action sequences, it is proved that the recognition algorithm based on correlation feature selection and multi-layer perceptron has a high recognition rate. The recognition rate of the algorithm proposed in this paper is as high as 96.667%.