基于伪三维卷积神经网络的手势姿态估计.

Most of the existing deep learning-based methods for hand pose estimation use a standard three -dimension convolutional neural network(3D CNN ) to extract 3D features and estimate the 3D coordinates of hand joints. The features extracted by these methods lack the multi-scale information of the hand, which limits the accuracy of hand pose estimation. In addition, due to the huge computational cost and memory requirements of the 3D CNN, these methods are often difficult to meet the realtime requirement. To overcome these weaknesses, the proposed method used a spatial filter and a depth filter to simulate 3D convolutions, which reduced the amount of parameters. It extracted and integrates features at various scales, making full use of the 3D information of hand pose. Experiments show that this method can improve estimation accuracy, reduce model size, and run at over 119 fps on a standard computer with a single GPU. [ABSTRACT FROM AUTHOR]