scholarly journals Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images

2014 ◽  
Vol 36 (11) ◽  
pp. 2131-2143 ◽  
Author(s):  
Matthias Dantone ◽  
Juergen Gall ◽  
Christian Leistner ◽  
Luc Van Gool
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
Body Parts ◽  
Still Images ◽  
Human Pose

scholarly journals Mixing body‐parts model for 2D human pose estimation in stereo videos

2017 ◽  
Vol 11 (6) ◽  
pp. 426-433 ◽  
Author(s):  
Manuel I. López‐Quintero ◽  
Manuel J. Marín‐Jiménez ◽  
Rafael Muñoz‐Salinas ◽  
Rafael Medina‐Carnicer
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
Body Parts ◽  
Human Pose

scholarly journals 2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images

2012 ◽  
Vol 99 (2) ◽  
pp. 190-214 ◽  
Author(s):  
M. Eichner ◽  
M. Marin-Jimenez ◽  
A. Zisserman ◽  
V. Ferrari
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
Still Images ◽  
Human Pose

scholarly journals Progressive Bi-C3D Pose Grammar for Human Pose Estimation

2020 ◽  
Vol 34 (07) ◽  
pp. 13033-13040 ◽  
Author(s):  
Lu Zhou ◽  
Yingying Chen ◽  
Jinqiao Wang ◽  
Hanqing Lu
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
Message Passing ◽  
Contextual Information ◽  
Human Pose Estimation ◽  
Body Parts ◽  
Multi Scale ◽  
Human Pose ◽  
Human Joints ◽  
Body Joints

In this paper, we propose a progressive pose grammar network learned with Bi-C3D (Bidirectional Convolutional 3D) for human pose estimation. Exploiting the dependencies among the human body parts proves effective in solving the problems such as complex articulation, occlusion and so on. Therefore, we propose two articulated grammars learned with Bi-C3D to build the relationships of the human joints and exploit the contextual information of human body structure. Firstly, a local multi-scale Bi-C3D kinematics grammar is proposed to promote the message passing process among the locally related joints. The multi-scale kinematics grammar excavates different levels human context learned by the network. Moreover, a global sequential grammar is put forward to capture the long-range dependencies among the human body joints. The whole procedure can be regarded as a local-global progressive refinement process. Without bells and whistles, our method achieves competitive performance on both MPII and LSP benchmarks compared with previous methods, which confirms the feasibility and effectiveness of C3D in information interactions.

scholarly journals 3D Human Pose Estimation in Vietnamese Traditional Martial Art Videos

2019 ◽  
Vol 3 (3) ◽  
pp. 471
Author(s):  
Tuong Thanh Nguyen ◽  
Van-Hung Le ◽  
Duy-Long Duong ◽  
Thanh-Cong Pham ◽  
Dung Le
Keyword(s):  
Pose Estimation ◽  
Martial Arts ◽  
Social Life ◽  
The Body ◽  
Human Pose Estimation ◽  
Body Parts ◽  
Creative Commons ◽  
Martial Art ◽  
Benchmark Datasets ◽  
Human Pose

Preserving, maintaining and teaching traditional martial arts are very important activities in social life. That helps preserve national culture, exercise and self-defense for practitioners. However, traditional martial arts have many different postures and activities of the body and body parts are diverse. The problem of estimating the actions of the human body still has many challenges, such as accuracy, obscurity, etc. In this paper, we survey several strong studies in the recent years for 3-D human pose estimation. Statistical tables have been compiled for years, typical results of these studies on the Human 3.6m dataset have been summarized. We also present a comparative study for 3-D human pose estimation based on the method that uses a single image. This study based on the methods that use the Convolutional Neural Network (CNN) for 2-D pose estimation, and then using 3-D pose library for mapping the 2-D results into the 3-D space. The CNNs model is trained on the benchmark datasets as MSCOCO Keypoints Challenge dataset [1], Human 3.6m [2], MPII dataset [3], LSP [4], [5], etc. We final publish the dataset of Vietnamese's traditional martial arts in Binh Dinh province for evaluating the 3-D human pose estimation. Quantitative results are presented and evaluated.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.  

scholarly journals A Novel Approach of Intelligent Computing for Multiperson Pose Estimation with Deep High Spatial Resolution and Multiscale Features

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haiquan Wang ◽  
Xiangyang Wang ◽  
Yijie Shi ◽  
Yanping Li ◽  
Chunhua Qian ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Pose Estimation ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
Human Pose Estimation ◽  
Body Parts ◽  
Low Resolution ◽  
Feature Maps ◽  
Intelligent Computing ◽  
Human Pose

Currently, human pose estimation (HPE) methods mainly rely on the design framework of Convolutional Neural Networks (CNNs). These CNNs typically consist of high-to-low-resolution subnetworks (encoder) to learn semantic information and low-to-high subnetworks (decoder) to raise the resolution for keypoint localization. Because too low-resolution feature maps in encoder will inevitably lose some spatial information, which cannot be recovered in the upsampling stages, keeping high spatial resolution features is critical for human pose estimation. On the other hand, due to scale variation of human body parts, multiscale features are also very important for human pose estimation. In this paper, a novel backbone network is proposed specifically for HPE, named High Spatial Resolution and Multiscale Networks (HSR-MSNet), which maintain high spatial resolution features in deeper layers of the encoder and meanwhile construct multiscale features within one single residual block via subgroup splitting and fusion of feature maps. Experiments show that our approach outperforms other state-of-the-art methods with more accurate keypoint locations on COCO dataset.

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