Academic Journal of Computing & Information Science, 2023, 6(13); doi: 10.25236/AJCIS.2023.061311.
Feng He1, Wenda Gao2, Yue You3
1College of Continuing Education, Civil Aviation Flight University of China, Guanghan, China
2Department of Atmospheric Sciences, Civil Aviation Flight University of China, Guanghan, China
3Department of Atmospheric Sciences, Civil Aviation Flight University of China, Guanghan, China
As people's attention to health and sports increases, the amount of data and audience of sports video is also growing rapidly. Based on this, the automatic description method of floor exercise video has attracted the attention of scientific researchers and industry. The research focus of this paper is on the automatic description of floors video, that is, to generate professional nouns by observing the movements of athletes in the video. This research has a wide range of application value, involving sports analysis, automatic interpretation and sports guidance. This paper specifically studies the automatic understanding of human movements in floor exercise videos, and combines the knowledge of computer vision and deep learning to realize the intelligent labeling and representation of specific human movements in video sequences.An automatic description method of floor exercise video based on three-dimensional convolutional network and multi-label classification is proposed. The floor exercise action is composed of multiple decomposition actions. In the work of this paper, a classifier of single decomposition action is constructed, and the automatic description problem of floor exercise action is transformed into a multi-label classification problem. Since the two-dimensional convolutional neural network loses time information when extracting features, this paper uses a three-dimensional convolutional network to extract spatio-temporal features of the video. Through multiple binary classifications, the goal of multi-label classification is achieved. In order to verify the effectiveness of the method, the classification results are randomly combined into a sentence, which is compared with the results of the automatic description method.
Floor exercise; Three-dimensional convolutional network; Multi-label classification; Automatic description
Feng He, Wenda Gao, Yue You. Automatic Description Method of Floor Exercise Video Based on Three-dimensional Convolutional Network and Multi-label Classification. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 13: 67-75. https://doi.org/10.25236/AJCIS.2023.061311.
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