Fast Monocular Hand Pose Estimation on Embedded Systems
This work addresses the need for fast and accurate hand pose estimation in human-robot interaction applications on embedded systems, representing an incremental improvement over prior methods.
The paper tackled the problem of hand pose estimation by proposing FastHand, a lightweight encoder-decoder framework that achieves high accuracy and runs at 25 frames per second on an NVIDIA Jetson TX2 GPU.
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.