組み込みシステム向け高速単眼ハンドポーズ推定
Fast Monocular Hand Pose Estimation on Embedded Systems
軽量なエンコーダ・デコーダ構成で、組み込みGPU上でも毎秒25フレームで動作する高精度な単眼ハンドポーズ推定手法を提案した論文。
著者: Shan An, Xiajie Zhang, Dong Wei, Haogang Zhu, Jianyu Yang, Konstantinos A. Tsintotas
分類: cs.RO, cs.CV
原文アブストラクト
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.
