View-invariant Pose Analysis for Human Movement Assessment from RGB Data

by   Fsardari, et al.

We propose a CNN regression method to generate high-level, view-invariant features from RGB images which are suitable for human pose estimation and movement quality analysis. The inputs to our net- work are body joint heatmaps and limb-maps to help our network ex- ploit geometric relationships between different body parts to estimate the features more accurately. A new multiview and multimodal human movement dataset is also introduced part of which is used to evaluate the results of the proposed method. We present comparative experimental results on pose estimation using a manifold-based pose representation built from motion-captured data. We show that the new RGB derived features provide pose estimates of similar or better accuracy than those produced from depth data, even from single views only.



There are no comments yet.


page 4


A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms

Many approaches have been proposed for human pose estimation in single a...

View Invariant 3D Human Pose Estimation

The recent success of deep networks has significantly advanced 3D human ...

A Generic Regression Framework for Pose Recognition on Color and Depth Images

Cascaded regression method is a fast and accurate method on finding 2D p...

Unsupervised 3D Pose Estimation for Hierarchical Dance Video Recognition

Dance experts often view dance as a hierarchy of information, spanning l...

VI-Net: View-Invariant Quality of Human Movement Assessment

We propose a view-invariant method towards the assessment of the quality...

Real-time RGBD-based Extended Body Pose Estimation

We present a system for real-time RGBD-based estimation of 3D human pose...

Occlusion-Invariant Rotation-Equivariant Semi-Supervised Depth Based Cross-View Gait Pose Estimation

Accurate estimation of three-dimensional human skeletons from depth imag...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.