基于CNN的人体姿态识别

姿态识别是人机交互中重要的研究课题之一,随着机器学习与神经网络的发展,研究的方式和成果趋于多样化,姿态识别的应用价值也日趋广泛。本文通过构建卷积神经网络模型,该模型共有11层,在对采样的数据集中5种人体姿态进行卷积与池化操作,最后进入全连接层进行分类,从而完成对数据集的训练和识别。结果显示,相较于机器学习方法,该模型的识别性能更加优秀,且免去了复杂的特征提取方式设计,让网络自身提取特征进行识别分类,效果更好。

Human Pose Recognition Based on CNN

Human posture recognition is one of the important research topics in human-computer interaction. With the development of machine learning and neural networks, the research methods and results tend to be diversified, and the application value of gesture recognition is becoming more and more extensive. This paper constructs a convolutional neural network model, which has 11 layers. It convolves and pools five kinds of human poses in the sampled data set, and finally enters the fully connected layer for classification, thus completing the training and identification of the data set. The results show that compared with the machine learning method, the recognition performance of the model is more excellent, and the complex feature extraction mode design is eliminated, so that the network itself extracts features to identify and classify better.