The advancements in modern science and technology have greatly promoted the progress of sports science. Advanced technological methods have been widely used in sports training, which have not only improved the scientific level of training but also promoted the continuous growth of sports technology and competition results. With the development of sports science and the gradual deepening of sport practices, the use of scientific training methods and monitoring approaches has improved the effect of sports training and athletes’ performance. This paper takes sprint as the research problem and constructs the image of sprinter’s action recognition based on machine learning. In view of the shortcomings of traditional dual-stream convolutional neural network for processing long-term video information, the time-segmented dual-stream network, based on sparse sampling, is used to better express the characteristics of long-term motion. First, the continuous video frame data is divided into multiple segments, and a short sequence of data containing user actions is formed by randomly sampling each segment of the video frame sequence. Next, it is applied to the dual-stream network for feature extraction. The optical flow image extraction involved in the dual-stream network is implemented by the system using the Lucas–Kanade algorithm. The system in this paper has been tested in actual scenarios, and the results show that the system design meets the expected requirements of the sprinters.

中文翻译：

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短跑运动员基于机器学习的动作识别算法

现代科学技术的进步极大地促进了体育科学的进步。先进的技术手段已广泛应用于运动训练中，不仅提高了训练的科学水平，而且促进了运动技术和比赛成绩的不断增长。随着体育科学的发展和体育实践的逐步深入，科学训练方法和监测方法的使用提高了体育训练和运动员成绩的效果。本文以冲刺为研究课题，并基于机器学习构建了短跑运动员动作识别的图像。鉴于传统的双流卷积神经网络在处理长期视频信息方面的缺点，因此，采用了时间分段双流网络，基于稀疏采样的方法，可以更好地表达长期运动的特征。首先，将连续的视频帧数据分成多个段，并通过随机采样视频帧序列的每个段来形成包含用户动作的短数据序列。接下来，将其应用于双流网络以进行特征提取。系统使用Lucas-Kanade算法实现了双流网络中涉及的光流图像提取。本文的系统已经在实际场景中进行了测试，结果表明该系统设计符合短跑运动员的预期要求。通过随机采样视频帧序列的每个片段，形成包含用户动作的简短数据序列。接下来，将其应用于双流网络以进行特征提取。系统使用Lucas-Kanade算法实现了双流网络中涉及的光流图像提取。本文的系统已经在实际场景中进行了测试，结果表明该系统设计符合短跑运动员的预期要求。通过随机采样视频帧序列的每个片段，形成包含用户动作的简短数据序列。接下来，将其应用于双流网络以进行特征提取。系统使用Lucas-Kanade算法实现了双流网络中涉及的光流图像提取。本文的系统已经在实际场景中进行了测试，结果表明该系统设计符合短跑运动员的预期要求。