scholarly journals 3D Single Person Pose Estimation Method Based on Deep Learning

2020 ◽  
Author(s):  
Xinrui Yuan ◽  
Hairong Wang ◽  
Jun Wang
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Estimation Method ◽  
Hybrid Network ◽  
Estimation Methods ◽  
Human Pose Estimation ◽  
Single Person ◽  
Depth Study ◽  
Human Pose ◽  
Set Up

In view of the significant effects of deep learning in graphics and image processing, research on human pose estimation methods using deep learning has attracted much attention, and many method models have been produced one after another. On the basis of tracking and in-depth study of domestic and foreign research results, this paper concentrates on 3D single person pose estimation methods, contrasts and analyzes three methods of end-to-end, staged and hybrid network models, and summarizes the characteristics of the methods. For evaluating method performance, set up an experimental environment, and utilize the Human3.6M data set to test several mainstream methods. The test results indicate that the hybrid network model method has a better performance in the field of human pose estimation.

scholarly journals Deep Learning Methods for 3D Human Pose Estimation under Different Supervision Paradigms: A Survey

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2267
Author(s):  
Dejun Zhang ◽  
Yiqi Wu ◽  
Mingyue Guo ◽  
Yilin Chen
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Literature Survey ◽  
Estimation Methods ◽  
Human Pose Estimation ◽  
Extensive Literature ◽  
Learning Methods ◽  
Human Pose ◽  
3D Human Pose Estimation ◽  
Research Studies

The rise of deep learning technology has broadly promoted the practical application of artificial intelligence in production and daily life. In computer vision, many human-centered applications, such as video surveillance, human-computer interaction, digital entertainment, etc., rely heavily on accurate and efficient human pose estimation techniques. Inspired by the remarkable achievements in learning-based 2D human pose estimation, numerous research studies are devoted to the topic of 3D human pose estimation via deep learning methods. Against this backdrop, this paper provides an extensive literature survey of recent literature about deep learning methods for 3D human pose estimation to display the development process of these research studies, track the latest research trends, and analyze the characteristics of devised types of methods. The literature is reviewed, along with the general pipeline of 3D human pose estimation, which consists of human body modeling, learning-based pose estimation, and regularization for refinement. Different from existing reviews of the same topic, this paper focus on deep learning-based methods. The learning-based pose estimation is discussed from two categories: single-person and multi-person. Each one is further categorized by data type to the image-based methods and the video-based methods. Moreover, due to the significance of data for learning-based methods, this paper surveys the 3D human pose estimation methods according to the taxonomy of supervision form. At last, this paper also enlists the current and widely used datasets and compares performances of reviewed methods. Based on this literature survey, it can be concluded that each branch of 3D human pose estimation starts with fully-supervised methods, and there is still much room for multi-person pose estimation based on other supervision methods from both image and video. Besides the significant development of 3D human pose estimation via deep learning, the inherent ambiguity and occlusion problems remain challenging issues that need to be better addressed.

scholarly journals Multi-View Pose Generator Based on Deep Learning for Monocular 3D Human Pose Estimation

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1116 ◽  
Author(s):  
Jun Sun ◽  
Mantao Wang ◽  
Xin Zhao ◽  
Dejun Zhang
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Data Augmentation ◽  
Estimation Method ◽  
Human Pose Estimation ◽  
Convolutional Network ◽  
3D Pose Estimation ◽  
Single View ◽  
Human Pose ◽  
3D Human Pose Estimation

In this paper, we study the problem of monocular 3D human pose estimation based on deep learning. Due to single view limitations, the monocular human pose estimation cannot avoid the inherent occlusion problem. The common methods use the multi-view based 3D pose estimation method to solve this problem. However, single-view images cannot be used directly in multi-view methods, which greatly limits practical applications. To address the above-mentioned issues, we propose a novel end-to-end 3D pose estimation network for monocular 3D human pose estimation. First, we propose a multi-view pose generator to predict multi-view 2D poses from the 2D poses in a single view. Secondly, we propose a simple but effective data augmentation method for generating multi-view 2D pose annotations, on account of the existing datasets (e.g., Human3.6M, etc.) not containing a large number of 2D pose annotations in different views. Thirdly, we employ graph convolutional network to infer a 3D pose from multi-view 2D poses. From experiments conducted on public datasets, the results have verified the effectiveness of our method. Furthermore, the ablation studies show that our method improved the performance of existing 3D pose estimation networks.

scholarly journals Human pose, hand and mesh estimation using deep learning: a survey

2022 ◽  
Author(s):  
Mukhiddin Toshpulatov ◽  
Wookey Lee ◽  
Suan Lee ◽  
Arousha Haghighian Roudsari
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Pose Estimation ◽  
State Of The Art ◽  
Significant Problem ◽  
Estimation Methods ◽  
Human Pose Estimation ◽  
Estimation Techniques ◽  
The Past ◽  
Human Pose

AbstractHuman pose estimation is one of the issues that have gained many benefits from using state-of-the-art deep learning-based models. Human pose, hand and mesh estimation is a significant problem that has attracted the attention of the computer vision community for the past few decades. A wide variety of solutions have been proposed to tackle the problem. Deep Learning-based approaches have been extensively studied in recent years and used to address several computer vision problems. However, it is sometimes hard to compare these methods due to their intrinsic difference. This paper extensively summarizes the current deep learning-based 2D and 3D human pose, hand and mesh estimation methods with a single or multi-person, single or double-stage methodology-based taxonomy. The authors aim to make every step in the deep learning-based human pose, hand and mesh estimation techniques interpretable by providing readers with a readily understandable explanation. The presented taxonomy has clearly illustrated current research on deep learning-based 2D and 3D human pose, hand and mesh estimation. Moreover, it also provided dataset and evaluation metrics for both 2D and 3DHPE approaches.

In-Cabin vehicle synthetic data to test Deep Learning based human pose estimation models

2021 ◽  
Author(s):  
Luis Gustavo Tomal Ribas ◽  
Marta Pereira Cocron ◽  
Joed Lopes Da Silva ◽  
Alessandro Zimmer ◽  
Thomas Brandmeier
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Synthetic Data ◽  
Human Pose Estimation ◽  
Human Pose ◽  
Estimation Models

Human Pose Estimation Based on Multi-resolution Feature Parallel Network for Public Security

2021 ◽  
Vol 7 (5) ◽  
pp. 1049-1058
Author(s):  
Xiangru Tao ◽  
Cheng Xu ◽  
Hongzhe Liu ◽  
Zhibin Gu
Keyword(s):  
Artificial Intelligence ◽  
Pose Estimation ◽  
Production Management ◽  
Estimation Method ◽  
Smoking Behavior ◽  
Input Image ◽  
Human Pose Estimation ◽  
Human Posture ◽  
Parallel Network ◽  
Human Pose

Smoking detection is an essential part of safety production management. With the wide application of artificial intelligence technology in all kinds of behavior monitoring applications, the technology of real-time monitoring smoking behavior in production areas based on video is essential. In order to carry out smoking detection, it is necessary to analyze the position of key points and posture of the human body in the input image. Due to the diversity of human pose and the complex background in general scene, the accuracy of human pose estimation is not high. To predict accurate human posture information in complex backgrounds, a deep learning network is needed to obtain the feature information of different scales in the input image. The human pose estimation method based on multi-resolution feature parallel network has two parts. The first is to reduce the loss of semantic information by hole convolution and deconvolution in the part of multi-scale feature fusion. The second is to connect different resolution feature maps in the output part to generate the high-quality heat map. To solve the problem of feature loss of previous serial models, more accurate human pose estimation data can be obtained. Experiments show that the accuracy of the proposed method on the coco test set is significantly higher than that of other advanced methods. Accurate human posture estimation results can be better applied to the field of smoking detection, and the smoking behavior can be detected by artificial intelligence, and the alarm will be automatically triggered when the smoking behavior is found.

Learning human poses in natural scenes

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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