基于3D卷积神经网络的动态手势识别

传统的2D卷积神经网络在进行视频识别时容易丢失目标在时间维度上的相关特征信息，导致识别准确率降低。针对该问题，本文采用3D卷积网络作为基本的网络框架，使用3D卷积核进行卷积操作提取视频中的时空特征，同时集成多个3D卷积神经网络模型对动态手势进行识别。为了提高模型的收敛速度和训练的稳定性，运用批量归一化（BN）技术优化网络，使优化后的网络训练时间缩短。实验结果表明，本文方法对于动态手势的识别具有较好的识别结果,在Sheffield Kinect Gesture (SKIG)数据集上识别准确率达到98.06%。与单独使用RGB信息、深度信息以及传统2D CNN相比，手势识别率均有所提高，验证了本文方法的可行性和有效性。

Dynamic Gesture Recognition Based on 3D Convolutional Neural Networks

In video recognition, the traditional 2D convolution neural networks are easy to lose the relevant feature information in time dimension, which leads to the reduction of recognition accuracy. This paper uses 3D convolutional neural network as a basic network framework with 3D convolution kernel to extract the temporal and spatial features of videos, at the same time, the integration of multiple 3D convolutional neural network models are proposed to recognize dynamic gesture. In order to improve the convergence speed of the model and the stability of training, the network is optimized by Batch Normalization (BN) technology to shorten the training time of the network. Experimental results show that the proposed method has a good recognition performance for dynamic gesture recognition, and the recognition accuracy reaches 98.06% in Sheffield Kinect Gesture (SKIG) data set. Solely compared with RGB information, depth information and traditional 2D CNN, the gesture recognition rate is higher, which verifies the feasibility and effectiveness of the proposed method.