【摘要】 针对传统卷积神经网络模型LeNet识别准确率低，占用内存大等问题，提出了一种基于轻量化卷积神经网络的人体动作识别模型(human activity recognition net, HARNet)。首先，利用MobileNetV2模型参数量和计算量小的特点，利用迁移学习方法，将预训练好的权重参数迁移到MobileNetV2模型中，最后添加全连接层构建了HARNet,实现了对日常行为动作的准确识别和分类。实验结果表明，该模型动作识别平均准确率可达89%,相比于传统卷积神经网络LeNet,准确率更高，且训练好的模型内存大小仅8.97 MB,验证了该模型的有效性。更多还原

【Abstract】 For the problems of low recognition accuracy and large memory consumption of the traditional convolutional neural network model LeNet, a human activity recognition net(HARNet) based on lightweight convolutional neural network was proposed.Firstly, the features of the MobileNetV2 model with a small number of parameters and calculations were used; then the transfer learning method was applied to transfer the pre-trained weight parameters to the MobileNetV2 model.Finally, a fully connected layer was added to build a HARNet, which achieved more accurate recognition and classification of daily behavioral actions.The experimental results show that the average accuracy of activity recognition of the model can reach 89%,which is higher than the traditional convolutional neural network LeNet, and the memory size of the trained model is only 8.97 MB,which verifies the effectiveness of the model.更多还原