针对双流网络对包含冗余信息的视频帧存在识别率低的问题，在双流网络的基础上引入scSE(Spatial and Channel Squeeze & Excitation Block)和非局部操作，构建SC_NLResNet行为识别框架。该框架将视频划分为等分不重叠的时序段并在每段上稀疏采样，提取RGB帧以及光流图作为scSE模块的输入;将经过scSE处理的特征输入非局部双流ResNet网络中，融合各分段得到最终的预测结果。在UCF101以及Hmdb51数据集上实验准确率分别达到96。9％和76。2％，结果表明，非局部操作与scSE模块结合可以增强特征时空上以及通道间的信息提高准确率，验证了 SC_NLResNet网络的有效性。
ACTION RECOGNITION ALGORITHM FOR NON-LOCAL TWO-STREAM RESNET NETWORK BASED ON SCSE FUSION
Aimed at the problem of low recognition rate of video frames containing redundant information in dual-stream network,scSE(Spatial and Channel Squeeze & Excitation Block)and non-local operation are introduced based on two-stream network to construct SC_NLResNet behavior recognition framework.In this framework,the framework divided the video into equal and non-overlapping temporal segments and sparsely sampled each segment,extracting RGB frames and optical flow graphs as the input of the scSE module.The features processed by scSE were inputted into the non-local two-stream ResNet network,and the segmentations were merged to obtain the final prediction results.The experimental accuracy on UCF101 and Hmdb51 dataset reaches 96.9％and 76.2％,respectively.The results show that the combination of non-local operation and scSE module can enhance the information of feature space-time and between the channels to improve the accuracy,which verifies the effectiveness of SC_NLResNet network.
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