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Motion Comparison Method Combining Segmented Multi-joint Line Graphs with the SIFT Matching Technique

机译:运动比较方法与SIFT匹配技术结合分段多关节线图

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In this paper, an effective motion comparison method based on segmented multi-joint line graphs combined with the SIFT feature matching method is proposed. Firstly, the multi-joint 3D motion data are captured using the Kinect. Secondly, 3D motion data are normalized and distortion data are removed. Therefore, a 2D line graph can be obtained. Next, SIFT features of the 2D motion line graph are extracted. Finally, the line graphs are divided into several regions and then the comparison results can be calculated based on SIFT matching ratios between the tutor's local line graph and the trainee's local line graph. The experimental results show that the proposed method not only can easily deal with the several challenge problems in motion analysis, e.g., the problem of different rhythm of motions, the problem of a large amount of data, but also can provide detailed error correction cues.
机译:本文提出了一种基于分段多关节线图与SIFT特征匹配方法组合的有效运动比较方法。首先,使用Kinect捕获多关节3D运动数据。其次,3D运动数据是归一化的,并且去除失真数据。因此,可以获得2D线图。接下来,提取2D运动线图的SIFT特征。最后,划分线图分为几个区域,然后可以基于导师的本地线图和学员的本地线图之间的SIFT匹配比来计算比较结果。实验结果表明,该方法不仅可以容易地处理运动分析中的几个挑战问题,例如,运动的问题不同的问题,还可以提供大量数据的问题,而且可以提供详细的纠错线索。

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