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首页> 外文期刊>Journal of robotics and mechatronics >Human Posture Probability Density Estimation Based on Actual Motion Measurement and Eigenpostures
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Human Posture Probability Density Estimation Based on Actual Motion Measurement and Eigenpostures

机译：基于实际运动测量和本征姿态的人体姿态概率密度估计

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摘要

We construct human posture probability density based on actual human motion measurement. Human postures in daily life were measured for two days by having subjects wear a mechanical motion capture device. Accumulated human postures were converted to unit quaternions to guarantee the uniqueness of posture representation. To represent probability density effectively, we propose eigenpostures for posture compression and use the kernel-based reduced set density estimator (RSDE) to reduce the number of posture samples and construction of posture probability density. Before compression, unit quaternions were converted to Euclidean space by logarithmic mapping. After conversion, postures were compressed in Euclidean space. Applying constructed human posture probability density for unlikely posture detection and motion segmentation, we verified its effectiveness for many different applications.
机译：我们基于实际人体运动测量来构建人体姿势概率密度。通过让受试者佩戴机械运动捕捉设备来测量两天中的人体姿势。累积的人体姿势被转换为单位四元数，以保证姿势表示的唯一性。为了有效地表示概率密度，我们提出了姿态压缩的特征姿势，并使用基于核的缩减集密度估计器（RSDE）来减少姿态样本的数量和构造姿态概率密度。在压缩之前，通过对数映射将单位四元数转换为欧几里得空间。转换后，姿势在欧几里得空间中被压缩。将构造的人体姿态概率密度应用于不太可能的姿态检测和运动分割，我们验证了其在许多不同应用中的有效性。

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