School of Physical Education, Liaocheng University, Liaocheng 252000, China.
Comput Intell Neurosci. 2022 May 14;2022:2839244. doi: 10.1155/2022/2839244. eCollection 2022.
The purpose is to effectively solve the problems of high time cost, low detection accuracy, and difficult standard training samples in video processing. Based on previous investigations, football game videos are taken as research objects, and their shots are segmented to extract the keyframes. The football game videos are divided into different semantic shots using the semantic annotation method. The key events and data in the football videos are analyzed and processed using a combination of artificial rules and a genetic algorithm. Finally, the performance of the proposed model is evaluated and analyzed by using concrete example videos as data sets. Results demonstrate that adding simple artificial rules based on the classic semantic annotation algorithms can save a lot of time and costs while ensuring accuracy. The target events can be extracted and located initially using a unique lens. The model constructed by the genetic algorithm can provide higher accuracy when the training samples are insufficient. The recall and precision of events using the text detection method can reach 96.62% and 98.81%, respectively. Therefore, the proposed model has high video recognition accuracy, which can provide certain research ideas and practical experience for extracting and processing affective information in subsequent videos.
目的在于有效解决视频处理中时间成本高、检测准确率低和标准训练样本难以获取的问题。基于前期调查，以足球比赛视频作为研究对象，对其镜头进行分割以提取关键帧。使用语义标注的方法对足球比赛视频进行不同语义镜头的划分。采用人工规则与遗传算法相结合的方式对足球视频中的关键事件和数据进行分析与处理。最后，使用具体的示例视频作为数据集对提出的模型的性能进行评估与分析。结果表明，在经典语义标注算法的基础上添加简单的人工规则可以在保证准确率的同时节省大量的时间与成本。利用独特的镜头可以初步提取和定位目标事件。在训练样本不足的情况下，遗传算法构建的模型可以提供更高的准确率。使用文本检测方法的事件召回率和准确率分别可以达到 96.62%和 98.81%。因此，该模型具有较高的视频识别准确率，可以为后续视频中情感信息的提取与处理提供一定的研究思路和实践经验。
