Classification and Recognition of Space Debris and Its Pose Estimation Based on Deep Learning of CNNs

2020 ◽  
pp. 605-613
Author(s):  
Roya Afshar ◽  
Shuai Lu
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Space Debris

scholarly journals 6D pose estimation with combined deep learning and 3D vision techniques for a fast and accurate object grasping

2021 ◽  
pp. 103775
Author(s):  
Tuan-Tang Le ◽  
Trung-Son Le ◽  
Yu-Ru Chen ◽  
Joel Vidal ◽  
Chyi-Yeu Lin
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
3D Vision

scholarly journals A Deep Learning Method for Target Pose Estimation of Planetary Surface

2021 ◽  
Vol 1948 (1) ◽  
pp. 012026
Author(s):  
Huang Lu ◽  
Mao Xiaoyan ◽  
Xing Yan
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Planetary Surface ◽  
Learning Method

scholarly journals Gait analysis for early Parkinson’s disease detection based on deep learning

2019 ◽  
Vol 5 (1) ◽  
pp. 9-12
Author(s):  
Jyothsna Kondragunta ◽  
Christian Wiede ◽  
Gangolf Hirtz
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Gait Analysis ◽  
Pose Estimation ◽  
Early Identification ◽  
Human Gait ◽  
3D Pose Estimation ◽  
Wearable Technologies ◽  
Advantages And Disadvantages

AbstractBetter handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this fact, early identification of symptoms for the PD plays a key role. Recent studies state that gait abnormalities are clearly evident while performing dual cognitive tasks by people suffering with PD. Researches also proved that the early identification of the abnormal gaits leads to the identification of PD in advance. Novel technologies provide many options for the identification and analysis of human gait. These technologies can be broadly classified as wearable and non-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep leaning has proven efficient in 2D pose estimation, we propose an 3D pose estimation along with proper dataset. This paper outlines the advantages and disadvantages of the state-of-the-art methods in application of gait analysis for early PD identification. Furthermore, the importance of extracting the gait parameters from 3D pose estimation using deep learning is outlined.

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

Deep-Learning Pipeline for Object Pose Estimation from an RGB-D Image

2021 ◽  
Vol 27 (8) ◽  
pp. 593-601
Author(s):  
You Chan No ◽  
YoungWoo Kim ◽  
Daegun Kim ◽  
Hyeon-Gyu Han ◽  
Young-ki Song ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Object Pose Estimation

Deep Learning-based Pose Estimation for Mobile Manipulator Tasks

2021 ◽  
Vol 45 (12) ◽  
pp. 1161-1166
Author(s):  
Hae-Chang Kim ◽  
In-Hwan Yoon ◽  
Jae-Bok Song
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Mobile Manipulator
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