Abstract
In order to solve the problem of human motion recognition in multimedia interaction scenarios in virtual reality environment, a motion classification and recognition algorithm based on linear decision and support vector machine (SVM) is proposed. Firstly, the kernel function is introduced into the linear discriminant analysis for nonlinear projection to map the training samples into a high-dimensional subspace to obtain the best classification feature vector, which effectively solves the nonlinear problem and expands the sample difference. The genetic algorithm is used to realize the parameter search optimization of SVM, which makes full use of the advantages of genetic algorithm in multi-dimensional space optimization. The test results show that compared with other classification recognition algorithms, the proposed method has a good classification effect on multiple performance indicators of human motion recognition and has higher recognition accuracy and better robustness.
Highlights
Today, with the rapid development of computer technology such as Internet of things (IoT), wireless communications, edge computing, and data mining [1–18], various advanced multimedia technologies emerge one after another
Many kinds of multimedia applications based on Virtual Reality (VR) technology have gradually become the hotspots of future cultural, art and entertainment markets, such as virtual shopping community, immersive virtual reality games, virtual landscape roaming and virtual art stage performances [22–24]
We proposes a human motion recognition method based on linear discriminant analysis (LDA) and support vector machine (SVM), in order to improve the efficiency and accuracy of human motion recognition in VR human–computer interaction applications
Summary
With the rapid development of computer technology such as Internet of things (IoT), wireless communications, edge computing, and data mining [1–18], various advanced multimedia technologies emerge one after another. In the virtual environment, the accurate recognition of human–computer interaction is especially important At this stage, mainstream human motion recognition methods mainly use machine vision technology, involving knowledge of advanced computer disciplines such as image processing, pattern recognition, and machine learning. We proposes a human motion recognition method based on LDA and SVM (named LDA-GA-SVM), in order to improve the efficiency and accuracy of human motion recognition in VR human–computer interaction applications. This method mainly studies from two aspects: (1) Improve the recognition rate of motion features. The global center c of A and the local center ci of each class Ai are respectively expressed as follows [34]
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