Voxelized In-Air Handwriting Recognition: Accurate 3D Pose Estimation and Recognition of Handwritten Gestures in Mid-Air Using V2V-PoseNet

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Mirzadeh Seyed Mojtaba; Zare Mohammad

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Journal Paper

Paper Information

Journal: NEW RESEARCHES IN THE SMART CITY Year:2023 | Volume:1 | Issue:4 Page(s): 45-52

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Information Journal Paper

Title

Voxelized In-Air Handwriting Recognition: Accurate 3D Pose Estimation and Recognition of Handwritten Gestures in Mid-Air Using V2V-PoseNet

Author(s)

Mirzadeh Seyed Mojtaba | Zare Mohammad | Issue Writer Certificate

Keywords

V2V-PoseNet

voxel-to-voxel prediction network

3d hand pose estimation

human pose estimation

single depth map

Abstract

Most of the existing deep learning-based methods for 3D hand pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of keypoints, such as hand or human body joints, via 2D convolutional neural networks (CNNs). The first weakness of this approach is the presence of perspective distortion in the 2D depth map. While the depth map is intrinsically 3D data, many previous methods treat depth maps as 2D images that can distort the shape of the actual object through projection from 3D to 2D space. This compels the network to perform perspective distortion-invariant estimation. The second weakness of the conventional approach is that directly regressing 3D coordinates from a 2D image is a highly nonlinear mapping, which causes difficulty in the learning procedure. To overcome these weaknesses, we firstly cast the 3D hand and human pose estimation problem from a single depth map into a voxel-to-voxel prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood for each keypoint. We design our model as a 3D CNN that provides accurate estimates while running in real-time.

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