基于双流网络与支持向量机融合的人体行为识别

传统的双流卷积神经网络存在难以理解长动作信息的问题,并且当长时间流信息损失时,模型泛化能力降低.针对此问题,文中提出基于双流网络与支持向量机融合的人体行为识别方法.首先,提取视频中每帧RGB图像及其对应垂直方向的稠密光流序列图,得到视频中动作的空间信息和时间信息,分别输入空间域和时间域网络进行预训练,预训练完成后进行特征提取.然后,针对双流网络提取的维度相同的特征向量执行并联融合策略,提高特征向量的表征能力.最后,将融合后的特征向量输入线性支持向量机中进行训练及分类处理.在KTH、UCF sports数据集上的实验表明文中方法具有较好的分类效果.

Human Action Recognition Fusing Two-Stream Networks and SVM

It is difficult for the traditional two-stream convolutional neural network to understand the long-motion information, and when the long-time stream information is lost, the generalization ability of the model decreases. Therefore, a method for human action recognition fusing two-stream network and support vector machine is proposed. Firstly, RGB images of each frame in the video and their corresponding dense optical flow sequence diagrams in the vertical direction are extracted, and the spatial information and time information of actions in the video are obtained. The information is input into the spatial domain and time domain networks for pre-training, and feature extraction is carried out after pre-training. Secondly, the feature vectors with the same dimension extracted from the two-stream network are fused in parallel to improve the representation ability of feature vectors. Finally, the fused feature vectors are input into the linear support vector machine for training and classification. The experimental results based on the standard open database proves that the classification effect of the proposed method is good.