A survey of human pose estimation: The body parts parsing based methods

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.

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

IET Computer Vision ◽

10.1049/iet-cvi.2016.0249 ◽

2017 ◽

Vol 11 (6) ◽

pp. 426-433 ◽

Cited By ~ 3

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

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Human pose estimation based on parallel atrous convolution and body structure constraints

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-212061 ◽

2021 ◽

pp. 1-11

Author(s):

Min Zhang ◽

Haijie Yang ◽

Pengfei Li ◽

Ming Jiang

Keyword(s):

Pose Estimation ◽

The Body ◽

Human Pose Estimation ◽

Spatial Relationships ◽

Body Structure ◽

Convolutional Network ◽

In Series ◽

Human Pose ◽

Ablation Study ◽

View Transformation

Human pose estimation is still a challenging task in computer vision, especially in the case of camera view transformation, joints occlusions and overlapping, the task will be of ever-increasing difficulty to achieve success. Most existing methods pass the input through a network, which typically consists of high-to-low resolution sub-networks that are connected in series. Still, during the up-sampling process, the spatial relationships and details might be lost. This paper designs a parallel atrous convolutional network with body structure constraints (PAC-BCNet) to address the problem. Among the mentioned techniques, the parallel atrous convolution (PAC) is constructed to deal with scale changes by connecting multiple different atrous convolution sub-networks in parallel. And it is used to extract features from different scales without reducing the resolution. Besides, the body structure constraints (BC), which enhance the correlation between each keypoint, are constructed to obtain better spatial relationships of the body by designing keypoints constraints sets and improving the loss function. In this work, a comparative experiment of the serial atrous convolution, the parallel atrous convolution, the ablation study with and without body structure constraints are conducted, which reasonably proves the effectiveness of the approach. The model is evaluated on two widely used human pose estimation benchmarks (MPII and LSP). The method achieves better performance on both datasets.

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Learning human poses in natural scenes

10.32469/10355/66196 ◽

2018 ◽

Author(s):

◽

Guanghan Ning

Keyword(s):

Computer Vision ◽

Pose Estimation ◽

The Body ◽

Human Pose Estimation ◽

Natural Scenes ◽

Top Down ◽

University Of Missouri ◽

Single Person ◽

Human Pose ◽

High Level

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The task of human pose estimation in natural scenes is to determine the precise pixel locations of body keypoints. It is very important for many high-level computer vision tasks, including action and activity recognition, human-computer interaction, motion capture, and animation. We cover two different approaches for this task: top-down approach and bottom-up approach. In the top-down approach, we propose a human tracking method called ROLO that localizes each person. We then propose a state-of-the-art single-person human pose estimator that predicts the body keypoints of each individual. In the bottomup approach, we propose an efficient multi-person pose estimator with which we participated in a PoseTrack challenge [11]. On top of these, we propose to employ adversarial training to further boost the performance of single-person human pose estimator while generating synthetic images. We also propose a novel PoSeg network that jointly estimates the multi-person human poses and semantically segment the portraits of these persons at pixel-level. Lastly, we extend some of the proposed methods on human pose estimation and portrait segmentation to the task of human parsing, a more finegrained computer vision perception of humans.

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Body parts relevance learning via expectation–maximization for human pose estimation

Multimedia Systems ◽

10.1007/s00530-021-00755-z ◽

2021 ◽

Author(s):

Luhui Yue ◽

Junxia Li ◽

Qingshan Liu

Keyword(s):

Pose Estimation ◽

Expectation Maximization ◽

Human Pose Estimation ◽

Body Parts ◽

Human Pose ◽

Relevance Learning

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Occlusion-free appearance modeling of body parts for human pose estimation

2015 14th IAPR International Conference on Machine Vision Applications (MVA) ◽

10.1109/mva.2015.7153195 ◽

2015 ◽

Cited By ~ 1

Author(s):

Yuki Kawana ◽

Norimichi Ukita ◽

Norihiro Hagita

Keyword(s):

Pose Estimation ◽

Human Pose Estimation ◽

Body Parts ◽

Appearance Modeling ◽

Human Pose

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Progressive Bi-C3D Pose Grammar for Human Pose Estimation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.7004 ◽

2020 ◽

Vol 34 (07) ◽

pp. 13033-13040 ◽

Cited By ~ 2

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.

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