Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-...Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.展开更多
In this paper,we first investigate the phenomenon of the spatial-temporal initialization dilemma towards realistic visual tracking,which may adversely affect tracking performance.We summarize the aforementioned phenom...In this paper,we first investigate the phenomenon of the spatial-temporal initialization dilemma towards realistic visual tracking,which may adversely affect tracking performance.We summarize the aforementioned phenomenon by comparing differences of the initialization manners in existing tracking benchmarks and in real-world applications.The existing tracking benchmarks provide offline sequences and the expert annotations in the initial frame for trackers.However,in real-world applications,a tracker is often initialized by user annotations or an object detector,which may provide rough and inaccurate initialization.Moreover,annotation from the external feedback also introduces extra time costs while the video stream will not pause for waiting.We select four representative trackers and conduct full performance comparison on popular datasets with simulated initialization to intuitively describe the initialization dilemma of the task.Then,we propose a simple compensation framework to address this dilemma.The framework contains spatial-refine and temporal-chasing modules to mitigate performance degradation caused by the initialization dilemma.Furthermore,the proposed framework can be compatible with various popular trackers without retraining.Extensive experiments verify the effectiveness of our compensation framework.展开更多
In this paper, we propose a Multi-granularity Spatial Access Control （MSAC） model, in which multi- granularity spatial objects introduce more types of policy rule conflicts than single-granularity objects do. To ana...In this paper, we propose a Multi-granularity Spatial Access Control （MSAC） model, in which multi- granularity spatial objects introduce more types of policy rule conflicts than single-granularity objects do. To analyze and detect these conflicts, we first analyze the conflict types with respect to the relationship among the policy rules, and then formalize the conflicts by template matrices. We designed a model-checking algorithm to detect potential conflicts by establishing formalized matrices of the policy set. Lastly, we conducted experiments to verify the performance of the algorithm using various spatial data sets and rule sets. The results show that the algorithm can detect all the formalized conflicts. Moreover, the algorithm＇s efficiency is more influenced by the spatial object granularity than the size of the rule set.展开更多
基金National Key Research and Development Program of China,No.2016YFB0502300。
文摘Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.
基金supported by the National Natural Science Foundation of China(No.U23A20384)the Talent Fund of Liaoning Province(No.XLYC2203014).
文摘In this paper,we first investigate the phenomenon of the spatial-temporal initialization dilemma towards realistic visual tracking,which may adversely affect tracking performance.We summarize the aforementioned phenomenon by comparing differences of the initialization manners in existing tracking benchmarks and in real-world applications.The existing tracking benchmarks provide offline sequences and the expert annotations in the initial frame for trackers.However,in real-world applications,a tracker is often initialized by user annotations or an object detector,which may provide rough and inaccurate initialization.Moreover,annotation from the external feedback also introduces extra time costs while the video stream will not pause for waiting.We select four representative trackers and conduct full performance comparison on popular datasets with simulated initialization to intuitively describe the initialization dilemma of the task.Then,we propose a simple compensation framework to address this dilemma.The framework contains spatial-refine and temporal-chasing modules to mitigate performance degradation caused by the initialization dilemma.Furthermore,the proposed framework can be compatible with various popular trackers without retraining.Extensive experiments verify the effectiveness of our compensation framework.
基金supported by the National Natural Science Foundation of China(Nos.51204185 and 41674030)Natural Youth Science Foundation of Jiangsu Province,China(No.BK20140185)+1 种基金China Postdoctoral Science Foundation(No.2016M601909)the Fundamental Research Funds for the Central Universities(No.2014QNA44)
文摘In this paper, we propose a Multi-granularity Spatial Access Control （MSAC） model, in which multi- granularity spatial objects introduce more types of policy rule conflicts than single-granularity objects do. To analyze and detect these conflicts, we first analyze the conflict types with respect to the relationship among the policy rules, and then formalize the conflicts by template matrices. We designed a model-checking algorithm to detect potential conflicts by establishing formalized matrices of the policy set. Lastly, we conducted experiments to verify the performance of the algorithm using various spatial data sets and rule sets. The results show that the algorithm can detect all the formalized conflicts. Moreover, the algorithm＇s efficiency is more influenced by the spatial object granularity than the size of the rule set.
