In this article, we implement an action recognition technique based on features fusion and best feature selection. In the proposed method, HSI color transformation is performed in the first step to improve the contrast of video frames and then extract their motion features by optical flow algorithm. The frames fusion approach extracts the moving regions that find out by optical flow. After that, extract shape and texture features fused by a new parallel approach name length control features. A new Weighted Entropy-Variances approach is applied to a combined vector and selects the best of them for classification. Finally, features are passed in M-SVM for final features classification into relevant human actions. The experimental process is conducted in four famous action datasets- Weizmann, KTH, UCF Sports, and UCF YouTube, with recognition rate 97.9%, 100%, 99.3%, and 94.5%, respectively. Experimental results show that the proposed scheme performed significantly sound output concerning listed methods.

在本文中，我们实现了一种基于特征融合和最佳特征选择的动作识别技术。在该方法中，首先进行HSI颜色转换，以提高视频帧的对比度，然后通过光流算法提取其运动特征。帧融合方法提取通过光流找出的运动区域。之后，提取形状和纹理特征，这些特征与新的并行方法名称长度控制特征融合在一起。一种新的加权熵方差方法被应用于组合向量，并从中选择最佳分类。最后，在M-SVM中传递特征，以将最终特征分类为相关的人类动作。实验过程在Weizmann，KTH，UCF Sports和UCF YouTube四个著名的动作数据集中进行，识别率达到97。分别为9％，100％，99.3％和94.5％。实验结果表明，所提出的方案在列出的方法方面表现出明显的声音输出。