On the Feasibility of Real-Time 3D Hand Tracking using Edge GPGPU Acceleration

TL;DR

This paper demonstrates that real-time 3D hand tracking can be effectively performed on weaker edge devices by offloading GPGPU computations to more powerful servers, enabling resource-constrained devices to achieve high performance.

Contribution

It introduces a method for porting a 3D hand tracking library to edge devices using Java-based offloading, addressing resource limitations through GPGPU computation offloading.

Findings

Weak edge devices can perform real-time 3D hand tracking via GPGPU offloading.

The offloading infrastructure effectively balances resource allocation between devices.

Challenges in information flow are identified and addressed for improved performance.

Abstract

This paper presents the case study of a non-intrusive porting of a monolithic C++ library for real-time 3D hand tracking, to the domain of edge-based computation. Towards a proof of concept, the case study considers a pair of workstations, a computationally powerful and a computationally weak one. By wrapping the C++ library in Java container and by capitalizing on a Java-based offloading infrastructure that supports both CPU and GPGPU computations, we are able to establish automatically the required server-client workflow that best addresses the resource allocation problem in the effort to execute from the weak workstation. As a result, the weak workstation can perform well at the task, despite lacking the sufficient hardware to do the required computations locally. This is achieved by offloading computations which rely on GPGPU, to the powerful workstation, across the network that connects them. We show the edge-based computation challenges associated with the information flow of the ported algorithm, demonstrate how we cope with them, and identify what needs to be improved for achieving even better performance.