点状特征柔性物体三维运动捕获方法

针对具有点状特征的柔性物体，提出了一种三维运动捕获方法.首先，该方法利用两个标定的高速摄像机拍摄柔性物体的运动视频，并对图像进行立体校正；然后，采用DOG （Difference Of Gaussian）算法获取点状特征的位置，并提取特征点极值；其次，在一定范围的窗口上搜索匹配对，匹配左右图像的特征点；再次，通过三角测量法进行三维重建；最后，利用搜索策略进行时间序列上的匹配，实现动态柔性物体的三维运动捕获，并计算空间坐标、速度、加速度参数.实验结果表明，相比于采用sift算法匹配特征点捕获柔性运动物体的方法，本方法精度更高.

3D Motion Capture Method of Point Features Flexible Object

In this study, a 3D motion capture method is proposed for a flexible object with point features. Firstly, the method utilizes two calibrated high-speed cameras to capture motion video of a flexible object, and makes a stereo rectification of the image. Secondly, through the Difference Of Gaussian (DOG) algorithm, the positions of the feature points are obtained, and the extreme of feature points is extracted. Thirdly, matching pairs in a certain range of windows are searched, the feature points of the left and right images are matched. Thirdly, 3D reconstruction is realized by triangulation. Finally, using the search strategy to match the time series, the 3D motion capture of a dynamic flexible object is realized, and the spatial coordinates, velocity, and acceleration parameters are calculated. The experimental results show that the method is more accurate than the method of using SIFT algorithm to match the feature points to capture the flexible moving object.