基于注意力机制的3D DenseNet人体动作识别方法

传统人体动作识别算法无法充分利用视频中人体动作的时空信息，且识别准确率较低。提出一种新的三维密集卷积网络人体动作识别方法。将双流网络作为基本框架，在空间网络中运用添加注意力机制的三维密集网络提取视频中动作的表观信息特征，结合时间网络对连续视频序列运动光流的运动信息进行特征提取，经过时空特征和分类层的融合后得到最终的动作识别结果。同时为更准确地提取特征并对时空网络之间的相互作用进行建模，在双流网络之间加入跨流连接对时空网络进行卷积层的特征融合。在UCF101和HMDB51数据集上的实验结果表明，该模型识别准确率分别为94.52%和69.64%，能够充分利用视频中的时空信息，并提取运动的关键信息。

Human Motion Recognition Method Based on Attention Mechanism of 3D DenseNet

Traditional human motion recognition algorithms cannot fully utilize the spatial and temporal information of human motions in videos,and are limited in the recognition accuracy.To address the problem,a three-dimensional dense convolutional network is proposed for recognizing human motions in videos.The model takes the two-stream network as the basic framework,using the 3D dense network with an attention mechanism for the spatial network to extract the apparent information features of human motions in the videos.On this basis,the temporal network is also used to extract the motion information features of the optical flows in the continuous video sequence.Then the spatio-temporal features and the classification layer are fused to obtain the final motion recognition accuracy.To extract features more accurately and model the interactions between the spatio-temporal networks,cross-stream connections are added between the two-stream networks to fuse the features at the convolutional layer of spatio-temporal networks.The experimental results show that the proposed model exhibits a recognition accuracy of 94.52% on the UCF101 dataset and 69.64% on the HMDB51 dataset.The model can make full use of the spatio-temporal information in the video to extract the key information of motions.