Human Movement Recognition Based On Fusion Features

With the rapid development of intelligent robot techology and its application in the field of manufacturing systems and human-computer interaction,intelligent human motion recognition technology provides an effective way to meet the high flexibility requirements of intelligent systems.In order to realize the effective recognition of the operator's actions,it is necessary to extract human action features that can completely and accurately describe the actions and realize the recognition and classification of the human actions.This article provides an in-depth analysis of different human movement characteristics,motion models,and parameter optimization methods.in this paper,the multi-features were fused and a motion recognition model was established.The parameters of the model were optimized to realize effective recognition of human motion.Based on the three-dimensional coordinates of human joints,the human body poses and movements are described through feature fusion,and a behavioral action model is established.Based on the simplification of the human skeleton model,a behavioral action model was established,and three complementary features of the human main joint angle,speed,and relative position were extracted and combined.The behavioral gesture was described through the fusion feature and the gesture was used to represent the action.In combination with the human motion video database,for ease of calculation,the Fourier transform is performed on each motion sample feature in the database with the most characteristic dimension sample as the standard,so that the feature dimensions of the motion samples are the same,and the sample data is normalized.Finally,principal component analysis is used to extract the main components of the feature,which reduces the feature dimesion and reduces redundant information.Based on the fusion feature,four multi-classification action recognition models were constructed,including one-to-one multi-classification model,one-to-many multi-classification model,directed acyclic multi-classification model,and decision tree multi-classification model.According to the different kernel functions in the multi-classification model,the behavior identification test was performed.The kernel function with the highest test and recognition accuracy was selected as the kernel function of the multi-classification model to realize the identification of test action samples.By comparing and analyzing the recognition results of different models,it is found that in the multi-classification recognition model,the one-to-many multi-classification model achieves a higher recognition rate.On the basis of analyzing the parameters of multi-class identification model,important parameters affecting the recognition accuracy rate are obtained.Using the improved grid optimization algorithm,firefly optimization algorithm,and wolves group optimization algorithm,the parameters of the multi-classification model are optimized,and the recognition rate of the multi-classification recognition model is improved.The speed of recognition in the optimization of wolves is optimized through experimental simulation and comparison.Faster,accurate,and robust.