Transparent AR Processing Acceleration at the Edge
Marco Trinelli, Massimo Gallo, Myriana Rifai, Fabio Pianese · 2019
Mobile devices are increasingly capable of supporting advanced functionalities but still face fundamental resource limitations. While the development of custom accelerators for compute-intensive functions is progressing, precious battery life and quality vs. latency trade-offs are limiting the potential of applications relying on processing real-time, computational-intensive functions, such as Augmented Reality. Transparent network support for on-the-fly media processing at the edge can significantly extend the capabilities of mobile devices without the need for API changes. In this paper we introduce NEAR, a framework for transparent live video processing and augmentation at the network edge, along with its architecture and preliminary performance evaluation in an object detection use case.