使用实时对象跟踪的智能手势

S. Bhat, N. Lavanya, M. Anusuya
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引用次数: 0

摘要

手势可以用于与计算机交互,而无需任何身体接触。可以尽量减少键盘和鼠标的使用。手势可以有多种类型。一种这样的类型是手在特定姿势下的运动。为了首先检测这些类型的手势,必须验证手是否出现在框架中并且以所需的姿势出现。第一种是通过在HSV颜色空间中考虑皮肤颜色范围来创建帧的掩码来实现的。后面的部分涉及到与一些模板形状的形状匹配。形状匹配涉及掩模和模板形状之间的中心矩的计算。手势定义手势的开始和结束。跟踪手势开始和结束之间的手的所有运动,并从跟踪的数据中识别手势。为了进行识别,使用了卷积神经网络。应用程序建立在识别的基础上。一旦识别出一个手势,就会触发一个事件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SMART GESTURE USING REAL TIME OBJECT TRACKING
Gesture can be used to interact with the computer without any physical contact. The use of keyboard and mouse can be minimized. Gesture can be of various types. One such type is movement of hand in a particular posture. To detect these type of gestures first it must be verified that the hand is present in frame and is present in the required posture. The first one is achieved by creating a mask of the frame considering the skin color range in the HSV color space. The later part involves shape matching with some template shape. The shape matching involves computing of central moments between the mask and the template shape. The hand posture defines the start and end of gesture. All the movement of hand between start and end of gesture is tracked and gesture is recognized from the tracked data. For the purpose of recognition, Convolution Neural Network is used. An application is built on recognition. Once a gesture is recognized an event will be triggered.
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