In radar automatic target recognition(RATR),the high-resolution range profile(HRRP)has garnered considerable attention owing to its minimal computational demands.However,radar HRRP target recognition still faces numer...In radar automatic target recognition(RATR),the high-resolution range profile(HRRP)has garnered considerable attention owing to its minimal computational demands.However,radar HRRP target recognition still faces numerous challenges,primarily due to substantial variations in the amplitude and distribution of HRRP scattering points because of slight azimuthal changes.To alleviate the effect of aspect sensitivity,a novel multi-frame attention network(MFA-Net)comprising a range deformable convolution module(RDCM),multi-frame attention module(MFAM),and global-local Transformer module(GLTM)is proposed.The RDCM is designed to adaptively learn the distance of scattering center migration.Subsequently,the MFAM extracts consistent features across different frames to alleviate the influence of power fluctuation.Finally,the GLTM allocates attention between global and local fea-tures.The feasibility and effectiveness of the proposed method are validated through simulation and experimental datasets,and the recognition rate is enhanced by more than 3%compared to the state-of-the-art methods.展开更多
In this paper,a novel multi-frame track-before-detect algorithm is proposed,which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise d...In this paper,a novel multi-frame track-before-detect algorithm is proposed,which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase.A criterion of track extrapolation is used to construct state transition set,root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space,then measurement plots of multi-frame are divided into several clusters,and finally multi-frame track-before-detect algorithm is implemented in each cluster.The computational complexity can be reduced by employing the proposed algorithm.Simulation results show that the proposed algorithm can accurately detect multiple targets in close proximity and reduce the number of false tracks.展开更多
Multi-frame coding is supported by the emerging H.264. It is important for the enhancement of both coding efficiency and error robustness. In this paper, error resilient schemes for H.264 based on multi-frame were inv...Multi-frame coding is supported by the emerging H.264. It is important for the enhancement of both coding efficiency and error robustness. In this paper, error resilient schemes for H.264 based on multi-frame were investigated. Error robust H.264 video transmission schemes were introduced for the applications with and without a feedback channel. The experimental results demonstrate the effectiveness of the proposed schemes.展开更多
Security surveillance of public scene is closely relevant to routine safety of individual.Under the stimulus of this concern,abnormal event detection is becoming one of the most important tasks in computer vision and ...Security surveillance of public scene is closely relevant to routine safety of individual.Under the stimulus of this concern,abnormal event detection is becoming one of the most important tasks in computer vision and video processing.In this paper,we propose a new algorithm to address the visual abnormal detection problem.Our algorithm decouples the problem into a feature descriptor extraction process,followed by an AutoEncoder based network called cascade deep AutoEncoder(CDA).The movement information is represented by a novel descriptor capturing the multi-frame optical flow information.And then,the feature descriptor of the normal samples is fed into the CDA network for training.Finally,the abnormal samples are distinguished by the reconstruction error of the CDA in the testing procedure.We validate the proposed method on several video surveillance datasets.展开更多
In this article,we present the realisation of a multi-frame and multi-dimensional WebGIS that allows users to simultaneously analyse a specific portion of the Earth taking into account the historical information,too.T...In this article,we present the realisation of a multi-frame and multi-dimensional WebGIS that allows users to simultaneously analyse a specific portion of the Earth taking into account the historical information,too.Two graphical panels have been realised:one for the usual 2D view and one for a more realistic 3D view.Both panels display historical maps of the city,the current orthophoto and the digital topographical map.The 3D frame is based on NASA World Wind,an open source virtual globe from where 3D buildings are shown extruding the 2D shapes using their mean height.Thanks to a specifically designed graphical user interface,it is also possible to dynamically thematise the buildings on the globe according to different criteria(e.g.the construction time span)so that only the geometries fulfilling the request are turned on.Within the proposed application,a synchronisation between the two panels has been implemented,in order to maintain a constant alignment of the two viewers.The application is also open to the time dimension.In fact,assigning to each geometry two dates(e.g.‘year of construction’and‘year of demolition’),it is possible to dynamically view how buildings have changed over time,both in their shape and height.Future developments of this work will concern the possibility of implementing a city model with a higher level of detail.展开更多
基金The National Natural Science Foundation of China(No.62388102)the Natural Science Foundation of Shandong Province(No.ZR2021MF134).
文摘In radar automatic target recognition(RATR),the high-resolution range profile(HRRP)has garnered considerable attention owing to its minimal computational demands.However,radar HRRP target recognition still faces numerous challenges,primarily due to substantial variations in the amplitude and distribution of HRRP scattering points because of slight azimuthal changes.To alleviate the effect of aspect sensitivity,a novel multi-frame attention network(MFA-Net)comprising a range deformable convolution module(RDCM),multi-frame attention module(MFAM),and global-local Transformer module(GLTM)is proposed.The RDCM is designed to adaptively learn the distance of scattering center migration.Subsequently,the MFAM extracts consistent features across different frames to alleviate the influence of power fluctuation.Finally,the GLTM allocates attention between global and local fea-tures.The feasibility and effectiveness of the proposed method are validated through simulation and experimental datasets,and the recognition rate is enhanced by more than 3%compared to the state-of-the-art methods.
基金supported by the Innovation Project of Science and Technology Commission of the Central Military Commission,China(No.19-HXXX-01-ZD-006-XXX-XX)。
文摘In this paper,a novel multi-frame track-before-detect algorithm is proposed,which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase.A criterion of track extrapolation is used to construct state transition set,root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space,then measurement plots of multi-frame are divided into several clusters,and finally multi-frame track-before-detect algorithm is implemented in each cluster.The computational complexity can be reduced by employing the proposed algorithm.Simulation results show that the proposed algorithm can accurately detect multiple targets in close proximity and reduce the number of false tracks.
文摘Multi-frame coding is supported by the emerging H.264. It is important for the enhancement of both coding efficiency and error robustness. In this paper, error resilient schemes for H.264 based on multi-frame were investigated. Error robust H.264 video transmission schemes were introduced for the applications with and without a feedback channel. The experimental results demonstrate the effectiveness of the proposed schemes.
基金the National Key R&D Program of China(2016YFE0204200)the National Natural Science Foundation of China(Grant Nos.61503017,U1435220)+2 种基金the Fundamental Research Funds for the Central Universities(YWF-14-RSC-102)the Aeronautical Science Foundation of China(2016ZC51022)the ANR AutoFerm project,the Platform CAPSEC funded by Region Champagne-Ardenne and FEDER.
文摘Security surveillance of public scene is closely relevant to routine safety of individual.Under the stimulus of this concern,abnormal event detection is becoming one of the most important tasks in computer vision and video processing.In this paper,we propose a new algorithm to address the visual abnormal detection problem.Our algorithm decouples the problem into a feature descriptor extraction process,followed by an AutoEncoder based network called cascade deep AutoEncoder(CDA).The movement information is represented by a novel descriptor capturing the multi-frame optical flow information.And then,the feature descriptor of the normal samples is fed into the CDA network for training.Finally,the abnormal samples are distinguished by the reconstruction error of the CDA in the testing procedure.We validate the proposed method on several video surveillance datasets.
文摘In this article,we present the realisation of a multi-frame and multi-dimensional WebGIS that allows users to simultaneously analyse a specific portion of the Earth taking into account the historical information,too.Two graphical panels have been realised:one for the usual 2D view and one for a more realistic 3D view.Both panels display historical maps of the city,the current orthophoto and the digital topographical map.The 3D frame is based on NASA World Wind,an open source virtual globe from where 3D buildings are shown extruding the 2D shapes using their mean height.Thanks to a specifically designed graphical user interface,it is also possible to dynamically thematise the buildings on the globe according to different criteria(e.g.the construction time span)so that only the geometries fulfilling the request are turned on.Within the proposed application,a synchronisation between the two panels has been implemented,in order to maintain a constant alignment of the two viewers.The application is also open to the time dimension.In fact,assigning to each geometry two dates(e.g.‘year of construction’and‘year of demolition’),it is possible to dynamically view how buildings have changed over time,both in their shape and height.Future developments of this work will concern the possibility of implementing a city model with a higher level of detail.
