基于分层教学法的仰泳训练中臂游轨迹定位研究

Tianxiang Liang
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引用次数: 0

摘要

传统的体育教学方式是教师向学生输出体育教学任务和方法,集中执行教学内容,集中反馈教学效果,集中评价的单向教学方式。在该教学模式下,通过分析仰泳训练臂的泳姿轨迹分布,结合学生的主观能动性,对仰泳训练臂的泳姿轨迹进行定位识别,并采用分层教学的方法对仰泳训练臂的泳姿轨迹进行检测,设计教学方案。提出了一种基于分层教学法的仰泳训练臂泳姿轨迹定位方法。建立了仰泳训练臂划水动作视觉图像的三维视觉曲面重建模型。结合仰泳训练臂划水动作视觉特征的采样结果,对仰泳训练臂划水动作视觉图像进行视觉跟踪和块匹配。采用模板自动匹配和小波多尺度分解方法对仰泳训练臂笔画运动图像的臂弧度边缘轮廓进行检测。建立了仰泳训练手臂划水动作视觉图像的视觉空间区域融合模型,根据正则化的定量特征分析结果自动定位了仰泳训练手臂划水动作视觉图像的弧线轨迹。仿真结果表明,该方法对仰泳训练臂的动作轨迹定位精度高,提高了对仰泳训练臂动作轨迹的跟踪和检测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Arm Stroke Track Positioning in Backstroke Training Based on Layered Teaching Method
Traditional physical education teaching method is one-way in which teacher's output physical education teaching tasks and methods to students, execute teaching contents, feedback teaching effects and evaluate them centrally. Under this teaching mode, by analyzing the distribution of stroke track of backstroke training arm, combining with students' subjective initiative, the stroke track of backstroke training arm is located and identified, and hierarchical teaching method is adopted to detect stroke track of backstroke training arm and design teaching plan. This paper puts forward a method of positioning the stroke track of backstroke training arm based on layered teaching method. A three-dimensional visual surface reconstruction model of the visual image of the stroke movement of backstroke training arm is constructed. Combining with the sampling results of the visual characteristics of the stroke movement of backstroke training arm, the visual tracking and block matching of the visual image of the stroke movement of backstroke training arm are carried out. The edge contour of arm radian of the stroke movement image of backstroke training arm is detected by template automatic matching and wavelet multi-scale decomposition method. The visual space region fusion model of the visual image of the backstroke training arm stroke movement is established, and the arc trajectory of the arm stroke movement visual image of the backstroke training arm is automatically located according to the regularized quantitative feature analysis results. The simulation results show that this method has high accuracy in positioning the stroke trajectory of backstroke training arm, and improves the ability of tracking and detecting the stroke trajectory of backstroke training arm.
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