Robust Human Pose Recognition Using Unlabelled Markers
Robust Human Pose Recognition Using Unlabelled Markers
复制标题
使用未标记的标记进行稳健的人体姿势识别
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
G. Qian
中科院分区：
文献类型：
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作者：
Yi Wang;G. Qian
在本文中，我们解决了强大的人体姿态识别使用未标记的标记从基于光学标记的运动捕捉系统。提出了一种由粗到精的快速位姿匹配算法。给定查询姿态，首先，根据沿着半径和高度维度的标记分布拒绝大多数不匹配的姿态。其次，使用基于使用快速傅立叶变换实现的循环卷积的快速直方图匹配方法估计查询姿态和其余候选姿态之间的相对旋转角度。最后，通过Levenberg-Marquardt最小化使用非线性最小二乘最小化来细化旋转角估计。在存在多个解决方案的情况下，可以通过对最小化的匹配分数进行阈值化来有效地去除假姿态。所提出的框架可以处理由遮挡引起的标记缺失。使用真实的运动捕捉数据的实验结果表明了该方法的有效性。
In this paper, we tackle robust human pose recognition using unlabelled markers obtained from an optical marker-based motion capture system. A coarse-to-fine fast pose matching algorithm is presented with the following three steps. Given a query pose, firstly, the majority of the non-matching poses are rejected according to marker distributions along the radius and height dimensions. Secondly, relative rotation angles between the query pose and the remaining candidate poses are estimated using a fast histogram matching method based on circular convolution implemented using the fast Fourier transform. Finally, rotation angle estimates are refined using nonlinear least square minimization through the Levenberg-Marquardt minimization. In the presence of multiple solutions, false poses can be effectively removed by thresholding the minimized matching scores. The proposed framework can handle missing markers caused by occlusion. Experimental results using real motion capture data show the efficacy of the proposed approach.