Human pose recognition based on multiple features and random forest algorithm
Fang Di, Hong Wang, Fanglong Meng · 2021
Human pose recognition based on bone node data collected by depth camera is a key problem in the field of human-computer interaction. To improve the accuracy of human pose recognition, a new algorithm based on multiple features and random forest model is proposed. Firstly, a 93-dimensional vector is defined, which contains the coordinate feature of the joint and the distance feature, and the distance feature is selected according to the spatial position of the joint. Then, in the process of body pose recognition, the random forest model is combined with Bagging algorithm to ensure the balance of samples, so as to improve the classification performance of the classifier for different samples. Finally, the performance test of the constructed classifier is carried out on the UTKinect-action3D Dataset. The experimental result shows that the algorithm can effectively identify a variety of human posture, and the recognition rate reaches more than 90%. The fusion of multiple features is of great significance to improve the accuracy of human posture recognition.