On the Feasibility of Real-Time 3D Hand Tracking using Edge GPGPU Acceleration
This addresses the challenge of resource allocation for edge-based computation in hand tracking, but it is incremental as it ports an existing library with minor improvements.
This paper tackled the problem of enabling real-time 3D hand tracking on computationally weak edge devices by offloading GPGPU computations to a powerful server, achieving successful execution on the weak workstation despite insufficient local hardware.
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.