Back To RGB: Deep articulated hand pose estimation from a single camera image

In this work, we demonstrate a method called Deep Hand Pose Machine(DHPM) that effectively detects the anatomical joints in the human hand based on single RGB images. Current state-of-the-art methods are able to robustly infer hand poses from RGB-D images. However, the depth map from an infrared cam...

Full description

Saved in:
Bibliographic Details
Main Authors: Ma, Wan-Duo Kurt, Lewis, J. P., Frean, Marcus, Balduzzi, David
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Data models
Electronics packaging
Image color analysis
Pose estimation
Training
Training data
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this work, we demonstrate a method called Deep Hand Pose Machine(DHPM) that effectively detects the anatomical joints in the human hand based on single RGB images. Current state-of-the-art methods are able to robustly infer hand poses from RGB-D images. However, the depth map from an infrared camera does not operate well under direct sunlight. Performing hand tracking outdoors using depth sensors results in unreliable depth information and inaccurate poses. For this reason we were motivated to create this method which solely utilizes ordinary RGB image without additional depth information. Our approach adapts the pose machine algorithm, which has been used in the past to detect human body joints. We perform pose machine training on synthetic data to accurately predict the position of the joints in a real hand image.
ISSN:2151-2205
DOI:10.1109/IVCNZ.2017.8402485