Application of Human Posture Recognition Method Integrated with Improved RF Algorithm in Modern Intelligent Engineering
Research on human pose recognition and modern intelligent engineering design has become an important research direc-tion in the field of human-computer interaction.In achieving more efficient and intelligent human pose recognition,this paper presents a classification trainer based on density-based spatial clustering of applications with random forest(DBSCAN-RF)algorithm,and im-proves the random forest(RF)algorithm by introducing the synthetic minority oversampling technique based on high-dimensional data(HD-SMOTE)method.The technological innovation and uniqueness of this method lies in the combination of density clustering and random forest advantages,which can effectively handle the datasets with noise and has high computational efficiency and scalability.Through the experimental testing,the recognition recall rate of the DBSCAN-RF algorithm reaches the highest level of 98.64％,which increases by 6.37％,4.28％,and 3.95％respectively compared with traditional RF algorithm,K-means-RF and Mean-shift-RF algorithm.Meanwhile,the recognition recall rate of the DBSCAN-RF algorithm reaches 95.31％and 96.48％for falls and normal walking,respectively.Moreover,the test time of the DBSCAN-RF algorithm is all lower than 62 ms.It meets the application of modern intelligent body posture recognition engineering,and provides a reliable technical support for modern intelligent body posture recognition.
DBSCAN-RFclassification trainerhuman pose recognitionmodern and intelligent engineering
NETL
NSTL
万方数据
