Fast Monocular Hand Pose Estimation on Embedded Systems

TL;DR

This paper introduces 'FastHand', a lightweight and efficient hand pose estimation framework suitable for embedded systems, achieving high accuracy and real-time performance on devices like NVIDIA Jetson TX2.

Contribution

The paper presents a novel lightweight encoder-decoder network architecture for fast and accurate monocular hand pose estimation on embedded devices.

Findings

Achieves 25 frames per second on NVIDIA Jetson TX2

Outperforms state-of-the-art methods in accuracy

Demonstrates effectiveness on public datasets

Abstract

Hand pose estimation is a fundamental task in many human-robot interaction-related applications. However, previous approaches suffer from unsatisfying hand landmark predictions in real-world scenes and high computation burden. This paper proposes a fast and accurate framework for hand pose estimation, dubbed as "FastHand". Using a lightweight encoder-decoder network architecture, FastHand fulfills the requirements of practical applications running on embedded devices. The encoder consists of deep layers with a small number of parameters, while the decoder makes use of spatial location information to obtain more accurate results. The evaluation took place on two publicly available datasets demonstrating the improved performance of the proposed pipeline compared to other state-of-the-art approaches. FastHand offers high accuracy scores while reaching a speed of 25 frames per second on an NVIDIA Jetson TX2 graphics processing unit.