基于卷积神经网络的手势识别算法设计

Design of Gesture Recognition Algorithm Based on Convolutional Neural Network

针对传感器对手势识别存在范围小、鲁棒性弱等痛点,采用MediaPipe机器学习框架对捕获的手势图像实时遍历,利用卷积神经网络进行高斯平滑滤波,并结合21个特征关节点手掌模型,根据欧氏空间距离判别阈值和单个手指曲率对指间动作做出分类,通过坐标关系建立指尖和模型特征点之间的实时映射。经测试,目标区域内手势识别准确精度达到98%,并实现了对控制音量大小等操作类手势的准确识别。

In response to the pain points of small range and weak robustness of sensors in gesture recognition,the MediaPipe machine learning framework is used to traverse the captured gesture images in real-time,and a convolutional neural network is used for Gaussian smoothing filtering.Combined with a palm model of 21 feature joint points,fingertip movements are classified based on Euclidean distance discrimination threshold and individual finger curvature,and real-time mapping between fingertips and model feature points is established through coordinate relationships.According to the test,the accuracy of gesture recognition in the target area has reached 98%,and the accurate recognition of operation gestures such as control volume is realized.