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Gesture Recognition Based on Kinect v2 and Leap Motion Data Fusion

机译:基于Kinect v2和Leap Motion数据融合的手势识别

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摘要

This study proposed a method for multiple motion-sensitive devices (i.e. one Kinect v2 and two Leap Motions) to integrate gesture data in Unity. Other depth cameras could replace the Kinect. The general steps in integrating gesture data for motion-sensitive devices were introduced as follows. (1) A method was proposed to recognize the fingertip from depth images for the Kinect v2. (2) Coordinates observed by three motion-sensitive devices were aligned in space in three steps. First, preliminary coordinate conversion parameters were obtained through joint calibration of the three devices. Second, two types of devices were approached to the observed value of the standard Leap Motion by the least squares method twice (i.e. one Kinect and one Leap Motion on the first round, then two Leap Motions on the second round). (3) Data of the three devices were aligned with time by using Unity while applying the data plan. On this basis, a human hand interacted with a virtual object in Unity. Experimental results demonstrated that the proposed method had a small recognition error of hand joints and realized the natural interaction between the human hand and virtual objects.
机译:这项研究提出了一种用于多个运动敏感设备(即一个Kinect v2和两个Leap Motion)的方法来在Unity中集成手势数据。其他深度相机可以代替Kinect。下面介绍了为运动敏感设备集成手势数据的一般步骤。 (1)提出了一种从Kinect v2的深度图像中识别指尖的方法。 (2)将三个运动敏感设备观察到的坐标在空间中分三步对齐。首先,通过三个设备的联合校准获得初步的坐标转换参数。其次,通过最小二乘法两次将两种类型的设备逼近标准的“跳跃运动”的观测值(即在第一轮中一次Kinect和一个“跳跃运动”,然后在第二轮中两次“跳跃运动”)。 (3)在应用数据计划时,使用Unity将三个设备的数据与时间对齐。在此基础上,人类的手与Unity中的虚拟对象进行了交互。实验结果表明,该方法对手关节的识别误差小,可以实现人手与虚拟物体之间的自然交互。

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