Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1801-1804. doi: 10.1109/EMBC48229.2022.9870876.
In recent years, markerless motion capture using a depth camera or RGB camera without any restriction on the subject has been attracting attention. Especially, depth cameras such as Kinect and RealSense allow instantaneous motion capture even at home outside lab environment, which is attractive for rehabilitation usage. However, single depth camera can capture steadily skeleton only when the subject stands facing to camera for the limited range, thus it is hard to apply to track skeletons while walking. Multiple depth cameras setting may allow to expand the range, but it can involve non-practical calibration process and can affect instantaneous capture advantage of depth camera. In this study, we propose a systematic method to integrate the motion information of skeletal models obtained from multiple depth cameras. The proposed method can perform a quick calibration using skeletal models instead of external reference objects, and estimate the spatial relationship of the sensors that allows the depth camera to move. The result demonstrates stable skeleton tracking free from occlusion problem keeping instantaneous capture capability of depth cameras.
近年来，使用深度相机或 RGB 相机进行无任何对象限制的无标记运动捕捉技术受到了广泛关注。特别是 Kinect 和 RealSense 等深度相机，即使在实验室环境之外的家庭中也可以实现即时运动捕捉，这对于康复应用非常有吸引力。然而，当被试面对相机站在有限的范围内时，单个深度相机才能稳定地捕捉骨骼，因此很难应用于跟踪行走时的骨骼。设置多个深度相机可以扩大范围，但可能涉及不实际的校准过程，并会影响深度相机的即时捕捉优势。在这项研究中，我们提出了一种系统的方法来整合来自多个深度相机的骨骼模型的运动信息。该方法可以使用骨骼模型快速进行校准，而无需外部参考对象，并估计允许深度相机移动的传感器的空间关系。结果表明，该方法能够稳定地跟踪骨骼，避免遮挡问题，同时保持深度相机的即时捕捉能力。
