Semantic Analysis of Multimodal Sports Video Based on the Support Vector Machine and Mobile Edge Computing

Abstract

Even though sports video analysis and research have yielded some results, the competition video itself contains a large number of images, video, audio, text, and other elements. Video semantic analysis has always been a hot and difficult topic in video studies. Multimodal sports video data contain a wealth of data. Second, it has a complex structure with an ambiguous relationship between each video unit. A typical algorithm based on kernel function learning is a support vector machine. It has shown promising results in the prediction of landslide displacement time series. The semantic analysis of multimodal sports video using support vector machines and mobile edge computing is the subject of this study. The theoretical prediction results based on the monitoring data of multimodal sports video semantic analysis show that they are comparable to neural network prediction results using genetic algorithm. The semantic analysis of multimodal sports video based on support vector machine method proposed in this study has a better prediction ability, and its theoretical prediction results are close to the actual monitoring values.