The visualization of flood disasters in virtual reality(VR)scenes is useful for the representation and sharing of disaster knowledge and can effectively improve users’cognitive efficiency in comprehending disaster in...The visualization of flood disasters in virtual reality(VR)scenes is useful for the representation and sharing of disaster knowledge and can effectively improve users’cognitive efficiency in comprehending disaster information.However,the existing VR methods of visualizing flood disaster scenes have some shortcomings,such as low rendering efficiency and poor user experience.In this paper,a tunnel vision optimization method for VR flood scenes based on Gaussian blur is proposed.The key techniques are studied,such as region of interest(ROI)calculation and tunnel vision optimization considering the characteristics of the human visual system.A prototype system has been developed and used to carry out an experimental case analysis.The experimental results show that the number of triangles drawn in a flood VR scene is reduced by approximately 30%–40%using this method and that the average frame rate is stable at approximately 90 frames per second(fps),significantly improving the efficiency of scene rendering and reducing motion sickness.展开更多
Background modeling and subtraction is a fundamental problem in video analysis. Many algorithms have been developed to date, but there are still some challenges in complex environments, especially dynamic scenes in wh...Background modeling and subtraction is a fundamental problem in video analysis. Many algorithms have been developed to date, but there are still some challenges in complex environments, especially dynamic scenes in which backgrounds are themselves moving, such as rippling water and swaying trees. In this paper, a novel background modeling method is proposed for dynamic scenes by combining both tensor representation and swarm intelligence. We maintain several video patches, which are naturally represented as higher order tensors,to represent the patterns of background, and utilize tensor low-rank approximation to capture the dynamic nature. Furthermore, we introduce an ant colony algorithm to improve the performance. Experimental results show that the proposed method is robust and adaptive in dynamic environments, and moving objects can be perfectly separated from the complex dynamic background.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.U2034202,41871289 and 41771442)the Sichuan Science and Technology Program(grant number2020JDTD0003).
文摘The visualization of flood disasters in virtual reality(VR)scenes is useful for the representation and sharing of disaster knowledge and can effectively improve users’cognitive efficiency in comprehending disaster information.However,the existing VR methods of visualizing flood disaster scenes have some shortcomings,such as low rendering efficiency and poor user experience.In this paper,a tunnel vision optimization method for VR flood scenes based on Gaussian blur is proposed.The key techniques are studied,such as region of interest(ROI)calculation and tunnel vision optimization considering the characteristics of the human visual system.A prototype system has been developed and used to carry out an experimental case analysis.The experimental results show that the number of triangles drawn in a flood VR scene is reduced by approximately 30%–40%using this method and that the average frame rate is stable at approximately 90 frames per second(fps),significantly improving the efficiency of scene rendering and reducing motion sickness.
基金supported by National Natural Science Foundation of China (Grant Nos. 11301137 and 11371036)the National Science Foundation of Hebei Province of China (Grant No. A2014205100
文摘Background modeling and subtraction is a fundamental problem in video analysis. Many algorithms have been developed to date, but there are still some challenges in complex environments, especially dynamic scenes in which backgrounds are themselves moving, such as rippling water and swaying trees. In this paper, a novel background modeling method is proposed for dynamic scenes by combining both tensor representation and swarm intelligence. We maintain several video patches, which are naturally represented as higher order tensors,to represent the patterns of background, and utilize tensor low-rank approximation to capture the dynamic nature. Furthermore, we introduce an ant colony algorithm to improve the performance. Experimental results show that the proposed method is robust and adaptive in dynamic environments, and moving objects can be perfectly separated from the complex dynamic background.
