Human Pose Regression by Combining Indirect Part Detection and Contextual Information

10/06/2017
by   Diogo C. Luvizon, et al.
0

In this paper, we propose an end-to-end trainable regression approach for human pose estimation from still images. We use the proposed Soft-argmax function to convert feature maps directly to joint coordinates, resulting in a fully differentiable framework. Our method is able to learn heat maps representations indirectly, without additional steps of artificial ground truth generation. Consequently, contextual information can be included to the pose predictions in a seamless way. We evaluated our method on two very challenging datasets, the Leeds Sports Poses (LSP) and the MPII Human Pose datasets, reaching the best performance among all the existing regression methods and comparable results to the state-of-the-art detection based approaches.

READ FULL TEXT

page 2

page 4

page 7

page 8

research
01/19/2022

Poseur: Direct Human Pose Regression with Transformers

We propose a direct, regression-based approach to 2D human pose estimati...
research
09/04/2020

SSP-Net: Scalable Sequential Pyramid Networks for Real-Time 3D Human Pose Regression

In this paper we propose a highly scalable convolutional neural network,...
research
10/26/2020

Handgun detection using combined human pose and weapon appearance

CCTV surveillance systems are essential nowadays to prevent and mitigate...
research
04/08/2021

Deep Monocular 3D Human Pose Estimation via Cascaded Dimension-Lifting

The 3D pose estimation from a single image is a challenging problem due ...
research
01/07/2019

Human Pose Estimation with Spatial Contextual Information

We explore the importance of spatial contextual information in human pos...
research
05/14/2019

Learnable Triangulation of Human Pose

We present two novel solutions for multi-view 3D human pose estimation b...
research
05/09/2021

Estimation of 3D Human Pose Using Prior Knowledge

Estimating three-dimensional human poses from the positions of two-dimen...

Please sign up or login with your details

Forgot password? Click here to reset