基于改进的深度卷积神经网络的人体动作识别方法

针对现有的动作识别算法的特征提取复杂、识别率低等问题，提出了基于批归一化变换(batch normalization)与GoogLeNet网络模型相结合的网络结构，将图像分类领域的批归一化思想应用到动作识别领域中进行训练算法改进，实现了对视频动作训练样本的网络输入进行微批量(mini-batch)归一化处理。该方法以RGB图像作为空间网络的输入，光流场作为时间网络输入，然后融合时空网络得到最终动作识别结果。在UCF101和HMDB51数据集上进行实验，分别取得了93.50%和68.32%的准确率。实验结果表明，改进的网络架构在视频人体动作识别问题上具有较高的识别准确率。

Action recognition base on improved deep convolutional neural network

Aiming at the problem of complex feature extraction and low accuracy in human action recognition, this paper proposed a network structure combining batch normalization algorithm with GoogLeNet network model. Applying Batch Normalization idea in the field of image classification to action recognition field, it improved the algorithm by normalizing the network input training sample by mini-batch. For convolutional network, RGB image was the spatial input, and stacked optical flows was the temporal input. Then, it fused the spatio-temporal networks to get the final action recognition result. It trained and evaluated the architecture on the standard video actions benchmarks of UCF101 and HMDB51, which achieved the accuracy of 93.50% and 68.32%. The results show that the improved convolutional neural network has a significant improvement in improving the recognition rate and has obvious advantages in action recognition.