Research On Real-Time Guarantee Mechanism Of Augmented Reality In Edge Computing Environment

Augmented Reality(AR)is a technology that combines virtual information with the real world.By using computer-generated images,sounds,and other sensory inputs,AR can enhance the perception experience in the real world.AR can be achieved through smartphones,tablets,head-mounted displays,and other devices.AR requires significant graphics processing power to recognize and track objects in the real world and combine virtual information with them.While the processing power of modern smartphones is continuously improving,it may still be insufficient for more complex applications.Offloading computation tasks to cloud servers can alleviate the processing power and storage capacity limitations of AR technology on mobile devices,but there are still issues such as latency and network bandwidth.Therefore,this paper proposes an AR registration and recognition real-time guarantee architecture based on mobile edge computing,and builds a mobile edge computing experimental platform.At the same time,a mobile AR application is developed to verify the superiority of the proposed architecture.The main work of this paper is as follows:1.In order to solve the problems of long response latency and high bandwidth demand caused by using cloud computing in traditional AR solutions,this paper proposes an adaptive cloud-edge collaborative AR module deployment scheme based on mobile edge computing.Firstly,this paper analyzes the commonly used modules in AR,and constructs a cloudedge collaborative AR application architecture based on edge computing methods such as collaborative caching and collaborative computing.For this architecture,this paper proposes an adaptive cloud-edge collaborative AR module deployment scheme.By considering network latency,network bandwidth,terminal computing power,and screen resolution parameters,this scheme adaptively deploys various modules of mobile AR,such as feature extraction,feature matching,object tracking,and information registration,on the cloud,edge,and terminal,thereby improving the overall performance of AR.2.In response to the adaptive AR module deployment scheme based on edge computing proposed in the previous section,this paper first constructed a mobile edge cloud architecture and developed a mobile AR app using Android.The app uses built-in algorithms from the OpenCV computer vision library to process the captured video frames.In this architecture,the multiple modules required for mobile AR are adaptively distributed among the cloud,edge,and terminal to ensure real-time performance and accuracy,thereby improving user experience.The experimental results show that the adaptive AR module deployment scheme based on edge computing can effectively reduce response time while ensuring accuracy,thereby improving overall performance.