基于时间网络的羽毛球视频动作识别

IF 0.5 Q4 ENGINEERING, MULTIDISCIPLINARY
Juncai Zhi, Zijie Sun, Ruijie Zhang, Zhouxiang Zhao
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引用次数: 1

摘要

随着人工智能研究的不断发展,计算机视觉研究已经从传统的基于“特征工程”的方法转向基于深度学习的“网络工程”方法,利用深度神经网络自动提取和分类特征。传统的基于人工设计特征的方法计算量大,通常用于解决简单的研究问题,不利于大规模的数据特征提取。基于深度学习的方法通过从大规模数据中学习特征,大大降低了人工特征的难度,并成功地应用于许多视觉识别任务中。视频动作识别方法也从传统的基于人工设计特征的方法向基于深度学习的方法转变,以构建更有效的深度神经网络模型为目标。通过对相关研究成果的收集和整理发现,学术界对于足球和篮球视频动作的定时片段网络研究比较丰富,而对于羽毛球的研究则缺乏针对上述研究成果的研究,本研究基于羽毛球视频动作定时片段网络的识别可以丰富研究成果,为后续研究提供参考。本文将轻量级注意机制引入到时间分割网络中,形成注意机制-时间分割网络,并对神经网络进行训练,得到羽毛球击球动作的分类器,可以预测羽毛球击球动作的正手、反手、顶球和挑球四种常见类型。实验结果表明,各种击球动作的识别召回率和正确率达到86%以上,平均召回率和正确率分别为91.2%和91.6%,表明基于时间分割网络的方法可以接近人的判断水平,可以有效地完成羽毛球视频击球动作的识别任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Badminton video action recognition based on time network
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.
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来源期刊
CiteScore
0.80
自引率
0.00%
发文量
152
期刊介绍: The major goal of the Journal of Computational Methods in Sciences and Engineering (JCMSE) is the publication of new research results on computational methods in sciences and engineering. Common experience had taught us that computational methods originally developed in a given basic science, e.g. physics, can be of paramount importance to other neighboring sciences, e.g. chemistry, as well as to engineering or technology and, in turn, to society as a whole. This undoubtedly beneficial practice of interdisciplinary interactions will be continuously and systematically encouraged by the JCMSE.
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