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 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 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 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.

scholarly journals The Aircraft Pose Estimation Based on a Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Daoyong Fu ◽  
Wei Li ◽  
Songchen Han ◽  
Xinyan Zhang ◽  
Zhaohuan Zhan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Pose Estimation ◽  
Estimation Method ◽  
Human Pose Estimation ◽  
Two Dimensional ◽  
Surveillance Video ◽  
Key Points ◽  
Human Pose

The pose estimation of the aircraft in the airport plays an important role in preventing collisions and constructing the real-time scene of the airport. However, current airport target surveillance methods regard the aircraft as a point, neglecting the importance of pose estimation. Inspired by human pose estimation, this paper presents an aircraft pose estimation method based on a convolutional neural network through reconstructing the two-dimensional skeleton of an aircraft. Firstly, the key points of an aircraft and the matching relationship are defined to design a 2D skeleton of an aircraft. Secondly, a convolutional neural network is designed to predict all key points and components of the aircraft kept in the confidence maps and the Correlation Fields, respectively. Thirdly, all key points are coarsely matched based on the matching relationship and then refined through the Correlation Fields. Finally, the 2D skeleton of an aircraft is reconstructed. To overcome the lack of benchmark dataset, the airport surveillance video and Autodesk 3ds Max are utilized to build two datasets. Experiment results show that the proposed method get better performance in terms of accuracy and efficiency compared with other related methods.

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.

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