Aiming at the problem of small area human occlusion in gait recognition,a method based on generating adversarial image inpainting network was proposed which can generate a context consistent image for gait occlusion a...Aiming at the problem of small area human occlusion in gait recognition,a method based on generating adversarial image inpainting network was proposed which can generate a context consistent image for gait occlusion area.In order to reduce the effect of noise on feature extraction,the stacked automatic encoder with robustness was used.In order to improve the ability of gait classification,the sparse coding was used to express and classify the gait features.Experiments results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA-B and TUM-GAID for gait recognition.展开更多
Against the background of regular epidemic prevention and control,in order to ensure the return of teachers to work,students to return to school and safe operation of schools,the risk of disease transmission is analyz...Against the background of regular epidemic prevention and control,in order to ensure the return of teachers to work,students to return to school and safe operation of schools,the risk of disease transmission is analyzed in key areas such as university canoons,auditoriums,teaching buildings and dormitories.The risk model of epidemic transmission in key regions of universities is established based on the improved SEIR model,considering the four groups of people,namely susceptible,latent,infected and displaced,and their mutual transformation relationship.After feature post-processing,the selected feature parameters are processed with monotone non-decreasing and smoothing,and used as noise-free samples of stacked sparse denoising automatic coding network to train the network.Then,the feature vectors after dimensionality reduction of the stacked sparse denoising automatic coding network are used as the input of the multi-hidden layer back-propagation neural network,and these features are used as tags to carry out fitting training for the network.The results show that the implementation of control measures can reduce the number of contacts between infected people and susceptible people,reduce the transmission rate of single contact,and reduce the peak number of infected people and latent people by 61%and 72%respectively,effectively controlling the disease spread in key regions of universities.Our method is able to accurately predict the number of infections.展开更多
基金Project(51678075) supported by the National Natural Science Foundation of ChinaProject(2017GK2271) supported by Hunan Provincial Science and Technology Department,China
文摘Aiming at the problem of small area human occlusion in gait recognition,a method based on generating adversarial image inpainting network was proposed which can generate a context consistent image for gait occlusion area.In order to reduce the effect of noise on feature extraction,the stacked automatic encoder with robustness was used.In order to improve the ability of gait classification,the sparse coding was used to express and classify the gait features.Experiments results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA-B and TUM-GAID for gait recognition.
基金supported by Key research Project of higher education institutions in Henan Province(Project:Name:A Study on Students’concentration in Class Based on Deep Multi-task Learning Framework,Project No.23B413004)the Science and Technology Project No.222102310222.
文摘Against the background of regular epidemic prevention and control,in order to ensure the return of teachers to work,students to return to school and safe operation of schools,the risk of disease transmission is analyzed in key areas such as university canoons,auditoriums,teaching buildings and dormitories.The risk model of epidemic transmission in key regions of universities is established based on the improved SEIR model,considering the four groups of people,namely susceptible,latent,infected and displaced,and their mutual transformation relationship.After feature post-processing,the selected feature parameters are processed with monotone non-decreasing and smoothing,and used as noise-free samples of stacked sparse denoising automatic coding network to train the network.Then,the feature vectors after dimensionality reduction of the stacked sparse denoising automatic coding network are used as the input of the multi-hidden layer back-propagation neural network,and these features are used as tags to carry out fitting training for the network.The results show that the implementation of control measures can reduce the number of contacts between infected people and susceptible people,reduce the transmission rate of single contact,and reduce the peak number of infected people and latent people by 61%and 72%respectively,effectively controlling the disease spread in key regions of universities.Our method is able to accurately predict the number of infections.
