.png){kind=icon}
.png){kind=icon}
Return
High Speed Object Tracking Using Edge Computing
.png){kind=icon}
PRE
AI
DOI：10.1145/3132211.3132457.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The use of unmanned aerial vehicles (UAV), or drones, has in recent years seen explosive growth due to lower costs and technology advances in mobile computing, batteries, sensors, and control systems. Drones are now used in a multitude of applications, from natural resource exploration, the film and entertainment industry, to urban surveillance, and defense. The image processing demands of these applications requires higher powered computing capabilities than those available locally to the drone, prompting the offloading of these tasks to the cloud. However, the latency requirements of the cloud are beyond those acceptable for many applications. This paper proposed the use of a server on the network edge to optimize both processing capability as well as latency for applications requiring real-time communication between a drone and a cloud server. We propose to test the limits of this model by implementing a system for real-time tracking of golf drives on a golf course.
Keywords:
Edge Computing
Computational Offloading
Drone
Unmanned Aerial Vehicles
Golf
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
P
IF:
0
Papers:
8
Citations:
0