With the development of optimization technology and the further recognition of neural network,ConvNeXt network is proposed and applied to visual classification tasks,and its performance surpassed a series of large-parameter and large-computational networks such as Transformer.Pose estimation task is the basic task in computer vision task and the basis of hand gesture recognition,which has a wide range of application prospects.In this paper,the ConvNeXt network is applied to hand pose estimation,and optimized.Heatmap encoding is introduced to increase the accuracy of key point coordinate prediction.Using the improved Adamw optimizer to optimize the model parameters,its PCK@0.2 index reached 0.992,and the EPE index also reached 3.47,surpassing the experimental results of other models.
Key words
deep learning /
pose estimation /
hand gesture recognition /
keypoint detection /
pattern recognition /
ConvNeXt
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