利用计算机视觉避免 COVID-19 传播的人工智能虚拟鼠标系统

Dr. Santhosh Kumar S
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引用次数: 0

摘要

在当代数字领域,提高人机交互的效率和直观性至关重要。传统的输入设备(如鼠标和键盘)正在被创新方法(如手势识别)所增强,后者提供了一种更自然的交互方式。本文旨在利用计算机视觉和深度学习技术生成一个由手势控制的虚拟鼠标。该系统利用网络摄像头捕捉用户手部动作的实时视频。这些动作通过卷积神经网络(CNN)进行分析,以识别特定手势,然后将其转化为鼠标操作,如光标移动、点击和滚动。该解决方案与硬件无关,只利用设备的摄像头,因此使用方便、直接。我们的目标是创造一种无缝、高效的交互方法,让用户可以在远处用简单的手势控制电脑。关键词卷积神经网络 深度学习 手势识别 虚拟鼠标 计算机视觉 OpenCV
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI Virtual Mouse System Using Computer Vision to avoid COVID-19 spread
In the contemporary digital landscape, enhancing human-computer interaction efficiency and intuitiveness is essential. Traditional input devices like mice and keyboards are being augmented by innovative approaches such as hand gesture recognition, which provides a more natural method of interaction. This paper aims to generate a virtual mouse controlled by hand gestures using computer vision and deep learning techniques. The system employs a webcam to capture live video of the user's hand movements. These movements are analyzed using convolutional neural networks (CNNs) to identify specific gestures, which are then translated into mouse operations like cursor movement, clicking, and scrolling. This solution is hardware-independent, utilizing only the device's camera, making it accessible and straightforward to use. The goal is to create a seamless and efficient interaction method, allowing users to control their computers with simple hand gestures from a distance. Keywords: Convolutional Neural Network, Deep Learning, Hand Gesture Recognition, Virtual Mouse, Computer Vision, OpenCV
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