Affiliations: [a] Department of Physical Education, Tangshan Normal University, Tangshan, Hebei, China | [b] Graduate School of Jeonju University, Jeonju, Korea
Correspondence:
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Corresponding author: Ruijie Zhang, Department of Physical Education, Tangshan Normal University, Tangshan, Hebei 063000, China. E-mail: zrj_ts@163.com.
Abstract: With the continuous development of artificial intelligence research, computer vision research has shifted from traditional “feature engineering”-based methods to deep learning-based “network engineering” methods, which automatically extracts and classifies features by using deep neural networks. Traditional methods based on artificial design features are computationally expensive and are usually used to solve simple research problems, which is not conducive for large-scale data feature extraction. Deep learning-based methods greatly reduce the difficulty of artificial features by learning features from large-scale data and are successfully applied in many visual recognition tasks. Video action recognition methods also shift from traditional methods based on artificial design features to deep learning-based methods, which is oriented to building more effective deep neural network models. Through collecting and sorting related research results found that academic for timing segment network of football and basketball video action research is relatively rich, but lack of badminton research given the above research results, this study based on timing segment network of badminton video action identification can enrich the research results, provide reference for follow-up research. This paper introduces the lightweight attention mechanism into the temporal segmentation network, forming the attention mechanism-timing segmentation network, and trains the neural network to get the classifier of badminton stroke action, which can be predicted as four common types: forehand stroke, backhand stroke, overhead stroke and pick ball. The experimental results show that the recognition recall and accuracy of various stroke movements reach more than 86%, and the average size of recall and accuracy is 91.2% and 91.6% respectively, indicating that the method based on timing segmentation network can be close to the human judgment level and can effectively conduct the identification task of badminton video strokes.
Keywords: Time segmentation network, video action recognition, action recognition
