Dataset creation method for hand pose estimation based on Mediapipe
Traditional hand gesture recognition methods have drawbacks such as large data requirements,many ineffective features,and high demand for annotated data.Additionally,the required hand movements to be recognized vary according to individual differences and customized needs.Furthermore,models trained on public datasets may not accurately predict hand pose in some special scenarios.At present,Google's Mediapipe hand detection model can directly obtain hand keypoint information through a well-trained algorithm.In this paper,we propose a convenient training dataset collection program based on the Mediapipe model,which can be used to train real-time hand pose detection algorithms in fixed scenes with low computational and resource costs.This approach not only improves accuracy,but also reduces data requirements and training time,so as to enhances the efficiency and reliability of the algorithm.Meanwhile,we also established a 10,classified gesture digital data set and carried out machine learning by multi-layer perceptron,achieving good results in recognition rate and sensitivity,with an accuracy rate of 93.38%.
hand pose detectiondatasetneural networkreal-time detection
万方数据
维普
