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.

中文翻译：

---

VR艺术媒体交互环境中基于支持向量机的人体运动识别

为了解决虚拟现实环境下多媒体交互场景下的人体运动识别问题，提出了一种基于线性决策和支持向量机的运动分类识别算法。首先，将核函数引入非线性投影的线性判别分析中，将训练样本映射到高维子空间中，以获得最佳的分类特征向量，有效地解决了非线性问题，扩大了样本差异。利用遗传算法来实现支持向量机的参数搜索优化，充分利用了遗传算法在多维空间优化中的优势。测试结果表明，与其他分类识别算法相比，