Motion history image (MHI) is a spatio-temporal template that temporal motion information is collapsed into a single image where intensity is a function of recency of motion. Also, it consists of spatial information. Energy image (EI) based on the magnitude of optical flow is a temporal template that shows only temporal information of motion. Each video can be described in these templates. So, four new methods are introduced in this study. The first three methods are called basic methods. In method 1, each video splits into N groups of consecutive frames and MHI is calculated for each group. Transfer learning with fine-tuning technique has been used for classifying these templates. EIs are used for classifying in method 2 similar to method 1. Fusing two streams of these templates is introduced as method 3. Finally, spatial information is added in method 4. Among these methods, method 4 outperforms others and it is called the proposed method. It achieves the recognition accuracy of 92.30 and 94.50% for UCF Sport and UCF-11 action data sets, respectively. Also, the proposed method is compared with the state-of-the-art approaches and the results show that it has the best performance.

中文翻译：

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使用全局描述符和预训练网络将视频分类问题转换为图像分类

运动历史图像（MHI）是时空模板，时空模板将时间运动信息折叠成单个图像，其中强度是运动的新近度的函数。而且，它由空间信息组成。基于光流大小的能量图像（EI）是仅显示运动的时间信息的时间模板。可以在这些模板中描述每个视频。因此，本研究引入了四种新方法。前三种方法称为基本方法。在方法1中，每个视频分为ñ多组连续帧，并为每组计算MHI。具有微调技术的转移学习已用于对这些模板进行分类。与方法1类似，在方法2中使用EI进行分类。方法3中引入了将这两个模板的流融合。最后，方法4中添加了空间信息。在这些方法中，方法4优于其他方法，被称为建议方法。对于UCF Sport和UCF-11动作数据集，它分别达到92.30％和94.50％的识别精度。此外，将所提出的方法与最新方法进行了比较，结果表明该方法具有最佳性能。