126 / 2022-10-21 09:31:59

Gesture Recognition Fusion Two-Stream 3D CNN and FPN

In the field of VR/AR human-computer interaction, natural and simple dynamic gesture recognition research has attracted much attention. In order to improve the accuracy of dynamic gesture recognition in human-computer interaction, this paper proposes a dynamic gesture recognition method FPN-3DResNeXt, which combines two-stream three-dimensional convolutional neural network (3DResNeXt) and feature pyramid (FPN). This method improves the structure of the 3DResNeXt network model, adds feature pyramid and attention channel, optimizes the model parameters, and then improves the recognition accuracy; in order to improve the convergence speed and stability of the model, it is proposed to add batch normalization (BN) Further optimization of the network reduces the training time. The experimental results show that the dynamic gesture recognition rate of the method proposed in this paper is 95.30%, which is 2.1% higher than that of the gesture recognition method based on 3DResNeXt by comparing with various 3D convolution methods on the EgoGesture dataset, and it has better stability.