Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In...Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.展开更多
Target tracking is one typical application of visual servoing technology. It is still a difficult task to track high speed target with current visual servo system. The improvement of visual servoing scheme is strongly...Target tracking is one typical application of visual servoing technology. It is still a difficult task to track high speed target with current visual servo system. The improvement of visual servoing scheme is strongly required. A position-based visual servo parallel system is presented for tracking target with high speed. A local Frenet frame is assigned to the sampling point of spatial trajectory. Position estimation is formed by the differential features of intrinsic geometry, and orientation estimation is formed by homogenous transformation. The time spent for searching and processing can be greatly reduced by shifting the window according to features location prediction. The simulation results have demonstrated the ability of the system to track spatial moving object.展开更多
AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 to...AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 total deviation values(TDVs)from the first 10 VF tests of the training dataset,VF points were clustered into several regions using the hierarchical ordered partitioning and collapsing hybrid(HOPACH)and K-means clustering.Based on the clustering results,a linear regression analysis was applied to each clustered region of the testing dataset to predict the TDVs of the 10th VF test.Three to nine VF tests were used to predict the 10th VF test,and the prediction errors(root mean square error,RMSE)of each clustering method and pointwise linear regression(PLR)were compared.RESULTS:The training group consisted of 228 patients(mean age,54.20±14.38y;123 males and 105 females),and the testing group included 81 patients(mean age,54.88±15.22y;43 males and 38 females).All subjects were diagnosed with POAG.Fifty-two VF points were clustered into 11 and nine regions using HOPACH and K-means clustering,respectively.K-means clustering had a lower prediction error than PLR when n=1:3 and 1:4(both P≤0.003).The prediction errors of K-means clustering were lower than those of HOPACH in all sections(n=1:4 to 1:9;all P≤0.011),except for n=1:3(P=0.680).PLR outperformed K-means clustering only when n=1:8 and 1:9(both P≤0.020).CONCLUSION:K-means clustering can predict longterm VF test results more accurately in patients with POAG with limited VF data.展开更多
Visual Attention Prediction(VAP)is widely applied in GIS research,such as navigation task identification and driver assistance systems.Previous studies commonly took color information to detect the visual saliency of ...Visual Attention Prediction(VAP)is widely applied in GIS research,such as navigation task identification and driver assistance systems.Previous studies commonly took color information to detect the visual saliency of natural scene images.However,these studies rarely considered adaptively feature integration to different geospatial scenes in specific tasks.To better predict visual attention while driving tasks,in this paper,we firstly propose an Adaptive Feature Integration Fully Convolutional Network(AdaFI-FCN)using Scene-Adaptive Weights(SAW)to integrate RGB-D,motion and semantic features.The quantitative comparison results on the DR(eye)VE dataset show that the proposed framework achieved the best accuracy and robustness performance compared with state-of-the-art models(AUC-Judd=0.971,CC=0.767,KL=1.046,SIM=0.579).In addition,the experimental results of the ablation study demonstrated the positive effect of the SAW method on the prediction robustness in response to scene changes.The proposed model has the potential to benefit adaptive VAP research in universal geospatial scenes,such as AR-aided navigation,indoor navigation,and street-view image reading.展开更多
A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future pred...A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem snurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the pre- diction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual ana- lytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summariza- tion of the predictive analytics workfiow.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 61175007the National Key Technologies R&D Program under Grant No. 2012BAH07B01the National Key Basic Research Program of China (973 Program) under Grant No. 2012CB316302
文摘Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.
基金This project is supported by National Electric Power Corporation Foundation of China(No.SPKJ010-27).
文摘Target tracking is one typical application of visual servoing technology. It is still a difficult task to track high speed target with current visual servo system. The improvement of visual servoing scheme is strongly required. A position-based visual servo parallel system is presented for tracking target with high speed. A local Frenet frame is assigned to the sampling point of spatial trajectory. Position estimation is formed by the differential features of intrinsic geometry, and orientation estimation is formed by homogenous transformation. The time spent for searching and processing can be greatly reduced by shifting the window according to features location prediction. The simulation results have demonstrated the ability of the system to track spatial moving object.
基金Supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),the Ministry of Health&Welfare,Republic of Korea(No.RS-2020-KH088726)the Patient-Centered Clinical Research Coordinating Center(PACEN),the Ministry of Health and Welfare,Republic of Korea(No.HC19C0276)the National Research Foundation of Korea(NRF),the Korea Government(MSIT)(No.RS-2023-00247504).
文摘AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 total deviation values(TDVs)from the first 10 VF tests of the training dataset,VF points were clustered into several regions using the hierarchical ordered partitioning and collapsing hybrid(HOPACH)and K-means clustering.Based on the clustering results,a linear regression analysis was applied to each clustered region of the testing dataset to predict the TDVs of the 10th VF test.Three to nine VF tests were used to predict the 10th VF test,and the prediction errors(root mean square error,RMSE)of each clustering method and pointwise linear regression(PLR)were compared.RESULTS:The training group consisted of 228 patients(mean age,54.20±14.38y;123 males and 105 females),and the testing group included 81 patients(mean age,54.88±15.22y;43 males and 38 females).All subjects were diagnosed with POAG.Fifty-two VF points were clustered into 11 and nine regions using HOPACH and K-means clustering,respectively.K-means clustering had a lower prediction error than PLR when n=1:3 and 1:4(both P≤0.003).The prediction errors of K-means clustering were lower than those of HOPACH in all sections(n=1:4 to 1:9;all P≤0.011),except for n=1:3(P=0.680).PLR outperformed K-means clustering only when n=1:8 and 1:9(both P≤0.020).CONCLUSION:K-means clustering can predict longterm VF test results more accurately in patients with POAG with limited VF data.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant No.42230103the State Key Laboratory of Geographic Information Engineering and the Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of the Ministry of Natural Resources Jointly Funded Project under Grant No.2021-04-03.
文摘Visual Attention Prediction(VAP)is widely applied in GIS research,such as navigation task identification and driver assistance systems.Previous studies commonly took color information to detect the visual saliency of natural scene images.However,these studies rarely considered adaptively feature integration to different geospatial scenes in specific tasks.To better predict visual attention while driving tasks,in this paper,we firstly propose an Adaptive Feature Integration Fully Convolutional Network(AdaFI-FCN)using Scene-Adaptive Weights(SAW)to integrate RGB-D,motion and semantic features.The quantitative comparison results on the DR(eye)VE dataset show that the proposed framework achieved the best accuracy and robustness performance compared with state-of-the-art models(AUC-Judd=0.971,CC=0.767,KL=1.046,SIM=0.579).In addition,the experimental results of the ablation study demonstrated the positive effect of the SAW method on the prediction robustness in response to scene changes.The proposed model has the potential to benefit adaptive VAP research in universal geospatial scenes,such as AR-aided navigation,indoor navigation,and street-view image reading.
基金This work was supported by National Basic Re- search Program of China （973 Program） （2015CB352503）, Major Pro- gram of the National Natural Science Foundation of China （61232012）, the National Natural Science Foundation of China （Grant Nos. 61303141, 61422211, u1536118, u1536119）, Zhejiang Provincial Natural Science Foundation of China （LR13F020001）, the Fundamental Research Funds for the Central Universities, the Innovation Joint Research Center for Cyber- Physical-Society System, and the United State＇s National Science Founda- tion （1350573）.
文摘A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem snurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the pre- diction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual ana- lytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summariza- tion of the predictive analytics workfiow.
