DEVELOPMENT AND RESEARCH OF THE MODEL FOR VIDEO INFORMATION CLASSIFICATION
Abstract
The article is devoted to solving the scientific problem of classifying video content in the face
of an increase in the information volume. Computer vision is a very relevant field of artificial intelligence
technologies application to expand the capabilities of various search and archive systems. The
authors give definitions to the main terms of the studied subject area. A formalized statement of the
problem to be solved is presented. A detailed classification of possible options for solving the problem
is given. With the rapid development of information technology, digital content is showing an
explosive growth trend. The classification of sports videos is of great importance for archiving digital
content on the server. Many data mining and machine learning algorithms have made great strides in
many application areas (such as classification, regression, and clustering). However, most of these
algorithms have a common drawback when the training and test samples are in the same feature
space and follow the same distribution. This article discusses the importance of solving the problem
of the video information content classification and automatic annotation, and also develops a model
based on deep learning and big data. As part of this study, the authors developed a model that improves
the quality of video classification, which improves search results. The results of the computational
experiment show that the proposed model can be effectively used to classify video events
within the sports subject area based on the use of a convolutional neural network. At the same
time, high accuracy of sports training video classification is provided. Compared with other models,
the proposed model has the advantages of simple implementation, fast processing speed, high
classification accuracy, and high generalization ability.
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