请用专业英语的被动语态和完成时态、过去时态翻译下面这段论文摘要。本设计利用OpenCV和MediaPipe技术实现了虚拟鼠标控制系统该系统能够通过实时捕捉手部动作和使用深度学习算法分析手部关键点判断六种不同的手部姿势并实现相应的鼠标操作包括移动、左击、右击、上滑、下滑、拖拽操作。本设计提高了传统鼠标控制方式的便利性和可靠性。在读取视频帧方面采用OpenCV调用摄像头实现并提取手部图像进行手势识别。

The virtual mouse control system was implemented using OpenCV and MediaPipe technologies, which captures real-time hand movements and uses deep learning algorithms to analyze hand key points to determine six different hand gestures and perform corresponding mouse operations, including movement, left click, right click, up slide, down slide, and drag. The traditional mouse control method has been improved in terms of convenience and reliability. OpenCV was used to call the camera to capture video frames and extract hand images for gesture recognition. MediaPipe was used to process hand images to extract finger key points, hand posture, and direction information, which is used to calculate the position and direction of the virtual mouse on the screen. Finally, the gesture and mouse action control logic was implemented using Python language and integrated with OpenCV and MediaPipe technologies to create a reliable and user-friendly virtual mouse system. The system has good smoothness and stability, and can be widely used in smart homes, smart healthcare, video games, and other fields to provide users with a more convenient operating experience