使用更快RCNN的手部识别和运动分析

M. Srividya, M. Anala, N. Dushyanth, Datla V. Satya K. Raju
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引用次数: 2

摘要

传统上,有线键盘和鼠标被用作计算机系统的输入设备。这些模型有许多缺点,比如硬件体积大,由于延迟而效率低下。在本文中,新提出的系统将使用手部识别和运动分析来输入数据。摄像头被用作捕捉手势的主要设备。对这些图像进行预处理以去除噪声并减少比特。图像预处理后,进行特征提取和分割。CNN算法通过识别手的特征来进行目标检测。为了让系统识别手势,需要使用许多图像来训练系统。这个过程被称为特征训练。一旦识别出特征和手势,就会将其映射到鼠标和键盘的特定功能上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hand Recognition and Motion Analysis using Faster RCNN
Traditionally wired keyboard and mouse were used as inputting devices to the computer system. These models had many drawbacks like hardware bulkiness and inefficiency due to delay. In this paper the new proposed system will input data using hand recognition and motion analysis. Webcam is used as the primary device to capture the hand gesture. These images are preprocessed to remove noise and for bit reduction. After the image preprocessing feature extraction and segmentation are done. CNN algorithms are used for object detection by identifying the features of the hand. For the system to identify the gesture, a number of images are used for training the system. This process is known as Features training. Once the features and the gestures are recognized, it is mapped to a particular function of the mouse and keyboard.
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