Recognition of human actions from visual contents is a budding field of computer vision and image understanding. The problem with such a recognition system is the huge dimensions of the feature vectors. Many of these features are irrelevant to the classification mechanism. For this reason, in this paper, we propose a novel feature selection (FS) model called cooperative genetic algorithm (CGA) to select some of the most important and discriminating features from the entire feature set to improve the classification accuracy as well as the time requirement of the activity recognition mechanism. In CGA, we have made an effort to embed the concepts of cooperative game theory in GA to create a both-way reinforcement mechanism to improve the solution of the FS model. The proposed FS model is tested on four benchmark video datasets named Weizmann, KTH, UCF11, HMDB51, and two sensor-based UCI HAR datasets. The experiments are conducted using four state-of-the-art feature descriptors, namely HOG, GLCM, SURF, and GIST. It is found that there is a significant improvement in the overall classification accuracy while considering very small fraction of the original feature vector.

从视觉内容识别人类行为是计算机视觉和图像理解的新兴领域。这种识别系统的问题是特征向量的巨大维度。这些功能中有许多与分类机制无关。因此，在本文中，我们提出了一种称为合作遗传算法（CGA）的新颖特征选择（FS）模型，以从整个特征集中选择一些最重要和最有区别的特征，以提高分类准确度和时间活动识别机制的要求。在CGA中，我们努力将合作博弈理论的概念嵌入GA中，以创建双向强化机制来改进FS模型的求解。拟议的FS模型在名为Weizmann，KTH的四个基准视频数据集上进行了测试，UCF11，HMDB51和两个基于传感器的UCI HAR数据集。实验是使用四个最新的特征描述符进行的，即HOG，GLCM，SURF和GIST。发现在考虑原始特征向量的很小部分的同时，总体分类精度有了显着提高。