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Robust hand detection using arm segmentation from depth data and static palm gesture recognition

机译:使用来自深度数据的手臂分割和静态手掌手势识别进行可靠的手部检测

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Nowadays there is a big interest in applying natural gesture communication language to various systems. There are several techniques on how to detect willed hand motion or recognize gestures by using different kind of sensors. RGB cameras can be used to detect hand, but it has a limited application. For example, hand detection can be particularly hard in some lighting conditions or in different skin colors. Using depth camera data, it is possible to distinguish object in front of camera in a better way. In this paper, we present a technique to detect active user and his hand gesture using both RGB and depth data. The approach firstly detects the user's face, mapping it to depth map data and finally uses the region from camera to person for human arm detection and hand palm recognition.
机译:如今,人们对将自然手势通信语言应用于各种系统非常感兴趣。关于如何通过使用不同种类的传感器来检测意愿的手部动作或识别手势的技术有多种。 RGB相机可用于检测手,但应用范围有限。例如,在某些照明条件下或在不同肤色下,手检测可能会特别困难。使用深度摄像机数据,可以更好地区分摄像机前面的对象。在本文中,我们提出了一种使用RGB和深度数据检测活动用户及其手势的技术。该方法首先检测用户的面部,将其映射到深度图数据,最后使用从相机到人的区域进行人手臂检测和手掌识别。

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