scholarly journals Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network

2014 ◽  
Author(s):  
Sijin LI ◽  
Zhi-Qiang Liu ◽  
Antoni B. Chan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pose Estimation ◽  
Deep Convolutional Neural Network ◽  
Human Pose Estimation ◽  
Task Learning ◽  
Human Pose

scholarly journals Human Pose Estimation-Based Real-Time Gait Analysis Using Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 191542-191550
Author(s):  
Ali Rohan ◽  
Mohammed Rabah ◽  
Tarek Hosny ◽  
Sung-Ho Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gait Analysis ◽  
Real Time ◽  
Pose Estimation ◽  
Human Pose Estimation ◽  
Human Pose

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.

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