Pose Tracking vs. Pose Estimation of AR Glasses with Convolutional, Recurrent, and Non-local Neural Networks: A Comparison

2021 ◽  
pp. 85-106
Author(s):  
Ahmet Firintepe ◽  
Sarfaraz Habib ◽  
Alain Pagani ◽  
Didier Stricker
Keyword(s):  
Neural Networks ◽  
Pose Estimation ◽  
Pose Tracking ◽  
Non Local

Driver pose estimation using convolutional neural networks

2021 ◽  
Vol 29 (4) ◽  
pp. 813-821
Author(s):  
Ren-wen CHEN ◽  
◽  
Ting-ting YUAN ◽  
Wen-bin HUANG ◽  
Yu-xiang ZHANG
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pose Estimation

scholarly journals Long-Range Pose Estimation for Aerial Refueling Approaches Using Deep Neural Networks

2020 ◽  
Vol 17 (11) ◽  
pp. 634-646
Author(s):  
Andrew Lee ◽  
Will Dallmann ◽  
Scott Nykl ◽  
Clark Taylor ◽  
Brett Borghetti
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Pose Estimation ◽  
Deep Neural Networks ◽  
Aerial Refueling

scholarly journals Improving Full-Body Pose Estimation from a Small Sensor Set Using Artificial Neural Networks and a Kalman Filter

2019 ◽  
Vol 33 ◽  
pp. 10063-10064
Author(s):  
Frank J. Wouda ◽  
Matteo Giuberti ◽  
Giovanni Bellusci ◽  
Bert-Jan F. Van Beijnum ◽  
Peter H. Veltink
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kalman Filter ◽  
Pose Estimation ◽  
Preliminary Results ◽  
State Prediction ◽  
Artificial Neural ◽  
Body Pose Estimation ◽  
Full Body

Previous research has shown that estimating full-body poses from a minimal sensor set using a trained ANN without explicitly enforcing time coherence has resulted in output pose sequences that occasionally show undesired jitter. To mitigate such effect, we propose to improve the ANN output by combining it with a state prediction using a Kalman Filter. Preliminary results are promising, as the jitter effects are diminished. However, the overall error does not decrease substantially.

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