基于视频深度学习的时空双流人物动作识别模型

深度学习在人物动作识别方面已取得较好的成效，但当前仍然需要充分利用视频中人物的外形信息和运动信息。为利用视频中的空间信息和时间信息来识别人物行为动作，提出一种时空双流视频人物动作识别模型。该模型首先利用两个卷积神经网络分别抽取视频动作片段空间和时间特征，接着融合这两个卷积神经网络并提取中层时空特征，最后将提取的中层特征输入到3D卷积神经网络来完成视频中人物动作的识别。在数据集UCF101和HMDB51上，进行视频人物动作识别实验。实验结果表明，所提出的基于时空双流的3D卷积神经网络模型能够有效地识别视频人物动作。

Spatio-temporal two-stream human action recognition model based on video deep learning

Deep learning has achieved good results in human action recognition, but it still needs to make full use of video human appearance information and motion information. To recognize human actions by using spatial information and temporal information in video, a video human action recognition model based on spatio-temporal two-stream was proposed. Two convolutional neural networks were used to extract spatial and temporal features of video sequences respectively in the proposed model, and then the two neural networks were merged to extract the middle spatio-temporal features, finally the video human action recognition was completed by inputting the extracted features into a 3D convolutional neural network. The video human action recognition experiments were carried out on the data set UCF101 and HMDB51. Experimental results show that the proposed 3D convolutional neural network model based on the spatio-temporal two-stream can effectively recognize the video human actions.