结合时序动态图和双流卷积网络的人体行为识别

为了更好地对人体动作的长时时域信息进行建模,提出了一种结合时序动态图和双流卷积网络的人体行为识别算法.首先,利用双向顺序池化算法来构建时序动态图,实现视频从三维空间到二维空间的映射,用来提取动作的表观和长时时序信息;然后提出了基于inceptionV3的双流卷积网络,包含表观及长时运动流和短时运动流,分别以时序动态图和...

Human Action Recognition Combining Sequential Dynamic Images and Two-Stream Convolutional Network

In order to well model the long-term time-domain information of human action,a human action recognition algorithm based on sequential dynamic images and two-stream convolution network is proposed.First of all,the sequential dynamic images are constructed by using sequential pooling algorithm to realize the mapping of video from three-dimensional space to two-dimensional space,which is used to extract the apparent and long-term sequential information of actions.Then,a two-stream convolution network based on inceptionV3 is proposed,which includes apparent and long-time motion flow and short-time motion flow.The input of the network is sequential dynamic images and stacked frame sequence of optical flow,and it combines data augmentation,pre-trained model,and sparse sampling.Finally,the classification judgment scores output by each branch is fused by average pooling.Experimental results on UCF101 and HMDB51 datasets show that,compared with the traditional two-stream convolution network,this method can effective use the temporal and spatial information of the action,and the recognition rate can be improved greatly,which shows effectiveness and robustness.