在Android设备上使用OpenCV的空中滑动手势识别

Twinkle Sharma, Sachin Kumar, Naveen Yadav, Kritika Sharma, P. Bhardwaj
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引用次数: 11

摘要

在这个不断变化的人机交互(HCI)技术时代,有必要加强人与计算机之间的沟通,这在很大程度上定义了人机交互(HCI),这有助于确定新的通信模型以及与机器交互的新方式。目前智能手机的输入方式仅限于物理按键、触摸屏、摄像头或内置传感器。智能手机在过去十年的快速发展主要是由于交互和视觉创新。例如,给定的输入方法要么需要专用的表面,要么需要用于交互的视线。但在当今智能手机或其他设备的可计算性不断提高,尺寸不断缩小的情况下,对这些设备的无触摸操作提出了需求。在这样的意图中,我们引入了Air-Swipe手势识别系统,它可以帮助用户在相机前做出In- air手势并进行不同的操作。该系统可以提供人性化的实时交互和可视化体验,增强了可用性,使android设备更具交互性。它不需要任何硬件改变,而是只使用设备的本机摄像头和机器学习软件,如开源计算机视觉(OpenCV)算法来检测环境的变化,并在不同的条件下做出相应的反应。我们对这种分类进行了测试,结果是考虑将帧沿x轴和y轴划分为象限,帧矩阵的值发生了变化。我们的方法识别手势的准确率接近96%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Air-swipe gesture recognition using OpenCV in Android devices
There is a need to enhance communication between human and computers which is being greatly defined in this changing era of technology with Human Computer Interaction (HCI) which is helping in determining new communication models and accordingly new ways of interacting with machines. Current smartphone inputs are limited to physical buttons, touchscreen input, cameras or built-in sensors. The rapid development of Smartphone's in the last decade was mainly due to interaction and visual innovations. For Example the given approaches of input either require a dedicated surface or Line-of-Sight for interaction. But in today's scenario of increasing computability of smartphone or other gadgets and their decreasing sizes have raised a need for such touch free operations over these gadgets. In such an intendment we introduce Air-Swipe Gesture Recognition System which can be useful to enable user to make In-Air gestures in front of the camera and to do different operations. This System can give a user-friendly and a live-experience of interaction and visualization, enhancing the usability and making the android device more interactive. It does not require any hardware changes instead only uses the native camera of the device and a machine learning software such as Open Source Computer Vision (OpenCV) algorithms to detect the changes in environment and respond accordingly in varying conditions. We tested this classification and found out the result that considering the frames to be divided into quadrants along x-axis and y-axis and found that the value of the frame matrix changes. Our approach has the capability of recognizing gestures with precision of almost 96%.
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