Abstract
Information technologies such as deep learning, big data, cloud computing, and the Internet of Things provide key technical tools to drive the rapid development of integrated manufacturing. In recent years, breakthroughs have been made in big data analysis using deep learning. The research on the sports video high‐precision classification model in this paper, more specifically, is the automatic understanding of human movements in free gymnastics videos. This paper will combine knowledge related to big data‐based computer vision and deep learning to achieve intelligent labeling and representation of specific human movements present in video sequences. This paper mainly implements an automatic narrative based on long‐ and short‐term memory networks to achieve the classification of sports videos. In the classical video description model S2VT, long‐ and short‐term memory networks are used to learn the mapping relationship between word sequences and video frame sequences. In this paper, we introduce an attention mechanism to highlight the importance of keyframes that determine freestyle gymnastic movements. In this paper, a dataset of freestyle gymnastics breakdown movements for professional events is built. Experiments are conducted on the data and the self‐constructed dataset, and the planned sampling method is applied to eliminate the differences between the training decoder and the prediction decoder. The experimental results show that the improved method in this paper can improve the accuracy of sports video classification. The video classification model based on big data and deep learning is to provide users with a better user experience and improve the accuracy of video classification. Also, in the experiments of this paper, the effect of extracting features for the classification of different lifting sports models is compared, and the effect of feature extraction network on the automatic description of free gymnastic movements is analyzed.
Highlights
With the rapid development of computers, networks, multimedia, and other related technologies, multimedia data has shown an exponential growth trend
The methods and related technical theories of automatic video description and video classification are introduced, and the main framework and implementation steps of the free gymnastics video automatic description method based on long- and short-term memory networks and the sports video automatic classification based on support vector machine multilabel classification are described in detail, and the feasibility of the improved methods in this paper is verified by comparison experiments
An automatic description method of free gymnastics based on big data and deep learning classification is proposed
Summary
With the rapid development of computers, networks, multimedia, and other related technologies, multimedia data has shown an exponential growth trend. Faced with the current problems in the research of sports video high-precision classification models [4], such as lowlevel video features cannot accurately reflect high-level human semantic concepts, high time complexity, and low recognition accuracy of action recognition algorithms in traditional RGB videos, and the use of single features cannot meet the massive growth of existing video data and its recognition of complex actions, the study of the automatic description of videos represented by competitive sports events has important theoretical research significance and extensive practical application value. This model achieves the same as the 2014 dual-stream method, accuracy of close video behavior classification It uses 3D convolution and 3D pooling and fully connected layers to form an 11-layer shallow network. The shallow network limits the classification performance of the model
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