融合双流三维卷积和注意力机制的动态手势识别

得益于计算机硬件以及计算能力的进步，自然、简单的动态手势识别在人机交互方面备受关注。针对人机交互中对动态手势识别准确率的要求，该文提出一种融合双流3维卷积神经网络(I3D)和注意力机制(CBAM)的动态手势识别方法CBAM-I3D。并且改进了I3D网络模型的相关参数和结构，为了提高模型的收敛速度和稳定性，使用了批量归一化(BN)技术优化网络，使优化后网络的训练时间缩短。同时与多种双流3D卷积方法在开源中国手语数据集(CSL)上进行了实验对比，实验结果表明，该文所提方法能很好地识别动态手势，识别率达到了90.76%，高于其他动态手势识别方法，验证了所提方法的有效性和可行性。

Dynamic Gesture Recognition Combining Two-stream 3D Convolution with Attention Mechanisms

Benefits from the progress of computer hardware and computing power, natural and simple dynamic gesture recognition gets a lot of attention in human-computer interaction. In view of the requirement of the accuracy of dynamic gesture recognition in human-computer interaction, a method of dynamic gesture recognition that combines Two-stream Inflated 3D (I3D) Convolution Neural Network (CNN) with the Convolutional Block Attention Module (CBAM-I3D) is proposed. In addition, relevant parameters and structures of the I3D network model are improved. In order to improve the convergence speed and stability of the model, the Batch Normalization (BN) technology is used to optimize the network, which shortens the training time of the optimized network. At the same time, experimental comparisons with various Two-stream 3D convolution methods on the open source Chinese Sign Language (CSL) recognition dataset are performed. The experimental results show that the proposed method can recognize dynamic gestures well, and the recognition rate reaches 90.76%, which is higher than other dynamic gesture recognition methods. The validity and feasibility of the proposed method are verified.