An action recognition network that combines multi-level spatiotemporal feature fusion with an attention mechanism is proposed as a solution to the issues of single spatiotemporal feature scale extraction,information r...An action recognition network that combines multi-level spatiotemporal feature fusion with an attention mechanism is proposed as a solution to the issues of single spatiotemporal feature scale extraction,information redundancy,and insufficient extraction of frequency domain information in channels in 3D convolutional neural networks.Firstly,based on 3D CNN,this paper designs a new multilevel spatiotemporal feature fusion(MSF)structure,which is embedded in the network model,mainly through multilevel spatiotemporal feature separation,splicing and fusion,to achieve the fusion of spatial perceptual fields and short-medium-long time series information at different scales with reduced network parameters;In the second step,a multi-frequency channel and spatiotemporal attention module(FSAM)is introduced to assign different frequency features and spatiotemporal features in the channels are assigned corresponding weights to reduce the information redundancy of the feature maps.Finally,we embed the proposed method into the R3D model,which replaced the 2D convolutional filters in the 2D Resnet with 3D convolutional filters and conduct extensive experimental validation on the small and medium-sized dataset UCF101 and the largesized dataset Kinetics-400.The findings revealed that our model increased the recognition accuracy on both datasets.Results on the UCF101 dataset,in particular,demonstrate that our model outperforms R3D in terms of a maximum recognition accuracy improvement of 7.2%while using 34.2%fewer parameters.The MSF and FSAM are migrated to another traditional 3D action recognition model named C3D for application testing.The test results based on UCF101 show that the recognition accuracy is improved by 8.9%,proving the strong generalization ability and universality of the method in this paper.展开更多
某风洞试验基地各试验外场分布在不同的物理区域,受多种条件制约,不宜采用集中式数据库,任务下达与数据上报采用手动分发与汇总,数据的时效性及完整性存在严重问题;分布式数据库可实现各场站的独立运行,但各场站与中心数据库的同步性、...某风洞试验基地各试验外场分布在不同的物理区域,受多种条件制约,不宜采用集中式数据库,任务下达与数据上报采用手动分发与汇总,数据的时效性及完整性存在严重问题;分布式数据库可实现各场站的独立运行,但各场站与中心数据库的同步性、完整性、一致性成为最大的难题;文章根据分布式数据库的特点和风洞试验运行需求,提出了基于MSF(microsoft sync framework)框架并利用WCF(windows communication foundation)技术实现了数据自动上传下达并汇总的同步设计方案,解决了中心与下级场站之间的数据管理问题。展开更多
基金supported by the General Program of the National Natural Science Foundation of China (62272234)the Enterprise Cooperation Project (2022h160)the Priority Academic Program Development of Jiangsu Higher Education Institutions Project.
文摘An action recognition network that combines multi-level spatiotemporal feature fusion with an attention mechanism is proposed as a solution to the issues of single spatiotemporal feature scale extraction,information redundancy,and insufficient extraction of frequency domain information in channels in 3D convolutional neural networks.Firstly,based on 3D CNN,this paper designs a new multilevel spatiotemporal feature fusion(MSF)structure,which is embedded in the network model,mainly through multilevel spatiotemporal feature separation,splicing and fusion,to achieve the fusion of spatial perceptual fields and short-medium-long time series information at different scales with reduced network parameters;In the second step,a multi-frequency channel and spatiotemporal attention module(FSAM)is introduced to assign different frequency features and spatiotemporal features in the channels are assigned corresponding weights to reduce the information redundancy of the feature maps.Finally,we embed the proposed method into the R3D model,which replaced the 2D convolutional filters in the 2D Resnet with 3D convolutional filters and conduct extensive experimental validation on the small and medium-sized dataset UCF101 and the largesized dataset Kinetics-400.The findings revealed that our model increased the recognition accuracy on both datasets.Results on the UCF101 dataset,in particular,demonstrate that our model outperforms R3D in terms of a maximum recognition accuracy improvement of 7.2%while using 34.2%fewer parameters.The MSF and FSAM are migrated to another traditional 3D action recognition model named C3D for application testing.The test results based on UCF101 show that the recognition accuracy is improved by 8.9%,proving the strong generalization ability and universality of the method in this paper.
文摘某风洞试验基地各试验外场分布在不同的物理区域,受多种条件制约,不宜采用集中式数据库,任务下达与数据上报采用手动分发与汇总,数据的时效性及完整性存在严重问题;分布式数据库可实现各场站的独立运行,但各场站与中心数据库的同步性、完整性、一致性成为最大的难题;文章根据分布式数据库的特点和风洞试验运行需求,提出了基于MSF(microsoft sync framework)框架并利用WCF(windows communication foundation)技术实现了数据自动上传下达并汇总的同步设计方案,解决了中心与下级场站之间的数据管理问题。
