A New Bottom-up Human Pose Estimation Method by Body Center and Anchor Points

2021 ◽  
Author(s):  
Jiahua Wu ◽  
Hyo Jong Lee
Keyword(s):  
Pose Estimation ◽  
Estimation Method ◽  
Human Pose Estimation ◽  
Bottom Up ◽  
Body Center ◽  
Anchor Points ◽  
Human Pose

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.

scholarly journals Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation

2021 ◽  
Author(s):  
Zhengxiong Luo ◽  
Zhicheng Wang ◽  
Yan Huang ◽  
Liang Wang ◽  
Tieniu Tan ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
Bottom Up ◽  
Human Pose

scholarly journals Human pose estimation method based on single depth image

2018 ◽  
Vol 12 (6) ◽  
pp. 919-924 ◽  
Author(s):  
Qingqiang Wu ◽  
Guanghua Xu ◽  
Min Li ◽  
Longting Chen ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Estimation Method ◽  
Depth Image ◽  
Human Pose Estimation ◽  
Human Pose

scholarly journals Articulated Human Pose Estimation Using Greedy Approach

2021 ◽  
Author(s):  
Pooja Kherwa ◽  
Sonali Singh ◽  
Saheel Ahmed ◽  
Pranay Berry ◽  
Sahil Khurana
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Parametric Representation ◽  
High Accuracy ◽  
Human Pose Estimation ◽  
Bottom Up ◽  
Global Context ◽  
Key Points ◽  
Robust Recognition ◽  
Human Pose

The goal of this Chapter is to introduce an efficient and standard approach for human pose estimation. This approach is based on a bottom up parsing technique which uses a non-parametric representation known as Greedy Part Association Vector (GPAVs), generates features for localizing anatomical key points for individuals. Taking leaf out of existing state of the art algorithm, this proposed algorithm aims to estimate human pose in real time and optimize its results. This approach simultaneously detects the key points on human body and associates them by learning the global context. However, In order to operate this in real environment where noise is prevalent, systematic sensors error and temporarily crowded public could pose a challenge, an efficient and robust recognition would be crucial. The proposed architecture involves a greedy bottom up parsing that maintains high accuracy while achieving real time performance irrespective of the number of people in the image.

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 Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression

2021 ◽  
Author(s):  
Zigang Geng ◽  
Ke Sun ◽  
Bin Xiao ◽  
Zhaoxiang Zhang ◽  
Jingdong Wang
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
Bottom Up ◽  
Human Pose

scholarly journals Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling Strategy

2021 ◽  
Author(s):  
Zihao Zhang ◽  
Lei Hu ◽  
Xiaoming Deng ◽  
Shihong Xia
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Fundamental Problem ◽  
Estimation Method ◽  
Sampling Strategy ◽  
Point Clouds ◽  
Human Pose Estimation ◽  
Human Pose ◽  
New Perspective ◽  
3D Human Pose Estimation

3D human pose estimation is a fundamental problem in artificial intelligence, and it has wide applications in AR/VR, HCI and robotics. However, human pose estimation from point clouds still suffers from noisy points and estimated jittery artifacts because of handcrafted-based point cloud sampling and single-frame-based estimation strategies. In this paper, we present a new perspective on the 3D human pose estimation method from point cloud sequences. To sample effective point clouds from input, we design a differentiable point cloud sampling method built on density-guided attention mechanism. To avoid the jitter caused by previous 3D human pose estimation problems, we adopt temporal information to obtain more stable results. Experiments on the ITOP dataset and the NTU-RGBD dataset demonstrate that all of our contributed components are effective, and our method can achieve state-of-the-art performance.

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