School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
China National Institute of Standardization, Beijing 100191, China.
Sensors (Basel). 2020 Feb 18;20(4):1119. doi: 10.3390/s20041119.
Microsoft Kinect, a low-cost motion capture device, has huge potential in applications that require machine vision, such as human-robot interactions, home-based rehabilitation and clinical assessments. The Kinect sensor can track 25 key three-dimensional (3D) "skeleton" joints on the human body at 30 frames per second, and the skeleton data often have acceptable accuracy. However, the skeleton data obtained from the sensor sometimes exhibit a high level of jitter due to noise and estimation error. This jitter is worse when there is occlusion or a subject moves slightly out of the field of view of the sensor for a short period of time. Therefore, this paper proposed a novel approach to simultaneously handle the noise and error in the skeleton data derived from Kinect. Initially, we adopted classification processing to divide the skeleton data into noise data and erroneous data. Furthermore, we used a Kalman filter to smooth the noise data and correct erroneous data. We performed an occlusion experiment to prove the effectiveness of our algorithm. The proposed method outperforms existing techniques, such as the moving mean filter and traditional Kalman filter. The experimental results show an improvement of accuracy of at least 58.7%, 47.5% and 22.5% compared to the original Kinect data, moving mean filter and traditional Kalman filter, respectively. Our method provides a new perspective for Kinect data processing and a solid data foundation for subsequent research that utilizes Kinect.
微软 Kinect 是一款低成本的运动捕捉设备，在需要机器视觉的应用中具有巨大的潜力，例如人机交互、家庭康复和临床评估。Kinect 传感器可以在每秒 30 帧的速度下跟踪人体 25 个关键的三维（3D）“骨骼”关节，并且骨骼数据通常具有可接受的准确性。然而，由于噪声和估计误差，从传感器获得的骨骼数据有时会表现出高水平的抖动。当存在遮挡或主体短时间稍微移出传感器的视野时，这种抖动会更严重。因此，本文提出了一种新的方法来同时处理来自 Kinect 的骨骼数据中的噪声和误差。最初，我们采用分类处理将骨骼数据分为噪声数据和错误数据。此外，我们使用卡尔曼滤波器来平滑噪声数据并校正错误数据。我们进行了遮挡实验以证明我们算法的有效性。与现有技术（如移动均值滤波器和传统卡尔曼滤波器）相比，所提出的方法的准确性至少提高了 58.7%、47.5%和 22.5%，分别为原始 Kinect 数据、移动均值滤波器和传统卡尔曼滤波器。我们的方法为 Kinect 数据处理提供了新的视角，并为后续利用 Kinect 的研究提供了坚实的数据基础。
