A Unified Deep Framework for Hand Pose Estimation and Dynamic Hand Action Recognition from First-Person RGB Videos

Viet-Duc Le, Van-Nam Hoang, Tien Nguyen, Van-Hung Le, Thanh-Hai Tran, Hai Vu, Thi-Lan Le
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引用次数: 4

Abstract

Understanding hand action from the first-person video has emerged recently thanks to its wide potential applications such as hand rehabilitation, augmented reality. The majority of works mainly reply on RGB images. Compared with RGB images, hand joints have certain advantages as they are robust to illuminations and appearance variation. However, previous works for hand action recognition usually employed hand joints that are manually determined. This paper presents a unified framework for both hand pose estimation and hand action recognition from first-person RGB images. First, our framework estimates 3D hand joints from every RGB image using a combination of Resnet and a Graphical convolutional network. Then, an adaptation of a SOTA method PA-ResGCN for the human skeleton is proposed for hand action recognition from estimated hand joints. Our framework takes advantage of efficient graphical networks to model graph-like human hand structure in both phases: hand pose estimation and hand action recognition. We evaluate the proposed framework on the First Person Hand Action Benchmark (FPHAB). The experiments show that the proposed framework outperforms different SOTA methods on both hand pose estimation and hand action recognition tasks.
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基于第一人称RGB视频的手部姿态估计和动态手部动作识别的统一深度框架
从第一人称视频中理解手部动作最近出现了,这要归功于它在手部康复、增强现实等领域的广泛潜在应用。大部分作品以RGB图像为主。与RGB图像相比,手关节对光照和外观变化具有一定的鲁棒性。然而,以往的手部动作识别工作通常采用手动确定的手关节。本文提出了一种基于第一人称RGB图像的手部姿态估计和手部动作识别的统一框架。首先,我们的框架使用Resnet和图形卷积网络的组合从每个RGB图像中估计3D手部关节。然后,提出了一种基于人体骨骼的SOTA方法PA-ResGCN的手部动作识别方法。我们的框架利用高效的图形网络在手部姿势估计和手部动作识别这两个阶段对类似图形的人手结构进行建模。我们在第一人称手动作基准(FPHAB)上评估了所提出的框架。实验表明,该框架在手部姿态估计和手部动作识别任务上均优于其他SOTA方法。
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