Automatic reconstruction of 3D human motion pose from uncalibrated monocular video sequences based on markerless human motion tracking
Automatic reconstruction of 3D human motion pose from uncalibrated monocular video sequences based on markerless human motion tracking
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DOI：
10.1016/j.patcog.2008.12.024
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发表时间：
2009-07
期刊：
影响因子：
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通讯作者：
Beiji Zou;Shu Chen;Cao Shi;Umugwaneza Marie Providence
中科院分区：
文献类型：
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作者：
Beiji Zou;Shu Chen;Cao Shi;Umugwaneza Marie Providence
提出了一种基于变形外观模型匹配的单目视频序列人体运动姿态重建方法。人体姿态估计是通过集成的人体关节跟踪与深度优先顺序的姿态重建。首先，基于人体骨架约束，利用逆运动学方法估计关节的欧拉角;然后，利用正向运动学方法确定场景中身体片段的像素点坐标，在透视投影的假设下，将场景中的这些像素点投影到图像平面上，得到图像中的变形外观模型区域。最后通过直方图匹配重建人体运动姿态。实验结果表明，该方法对多个复杂的人体运动序列都能获得良好的重建效果。
We present a method to reconstruct human motion pose from uncalibrated monocular video sequences based on the morphing appearance model matching. The human pose estimation is made by integrated human joint tracking with pose reconstruction in depth-first order. Firstly, the Euler angles of joint are estimated by inverse kinematics based on human skeleton constrain. Then, the coordinates of pixels in the body segments in the scene are determined by forward kinematics, by projecting these pixels in the scene onto the image plane under the assumption of perspective projection to obtain the region of morphing appearance model in the image. Finally, the human motion pose can be reconstructed by histogram matching. The experimental results show that this method can obtain favorable reconstruction results on a number of complex human motion sequences.