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首页> 外文期刊>IEEE transactions on visualization and computer graphics >Motion Capture With Ellipsoidal Skeleton Using Multiple Depth Cameras
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Motion Capture With Ellipsoidal Skeleton Using Multiple Depth Cameras

机译：使用多个深度相机的椭球骨架进行运动捕捉

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This paper introduces a novel motion capturing framework which works by minimizing the fitting error between an ellipsoid based skeleton and the input point cloud data captured by multiple depth cameras. The novelty of this method comes from that it uses the ellipsoids equipped with the spherical harmonics encoded displacement and normal functions to capture the geometry details of the tracked object. This method is also integrated with a mechanism to avoid collisions of bones during the motion capturing process. The method is implemented parallelly with CUDA on GPU and has a fast running speed without dedicated code optimization. The errors of the proposed method on the data from Berkeley Multimodal Human Action Database (MHAD) are within a reasonable range compared with the ground truth results. Our experiment shows that this method succeeds on many challenging motions which are failed to be reported by Microsoft Kinect SDK and not tested by existing works. In the comparison with the state-of-art marker-less depth camera based motion tracking work our method shows advantages in both robustness and input data modality.
机译：本文介绍了一种新颖的运动捕捉框架，该框架通过最小化基于椭球的骨架与多个深度相机捕捉的输入点云数据之间的拟合误差来工作。这种方法的新颖性在于它使用配备了球谐编码位移和法线函数的椭球体来捕获被跟踪物体的几何细节。此方法还与一种机制集成在一起，可避免在运动捕获过程中骨骼发生碰撞。该方法与GPU上的CUDA并行实现，运行速度快，无需专门的代码优化。与地面真实结果相比，该方法对伯克利多模式人类行为数据库（MHAD）数据的误差在合理范围内。我们的实验表明，这种方法在许多具有挑战性的动作上都取得了成功，这些动作未能通过Microsoft Kinect SDK进行报告，也未经现有作品进行测试。与基于最新技术的无标记深度相机的运动跟踪工作相比，我们的方法在鲁棒性和输入数据模态方面均具有优势。

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