scholarly journals Lower Body Pose Estimation in Team Sports Videos Using Label-Grid Classifier Integrated with Tracking-by-Detection

2015 ◽  
Vol 7 (0) ◽  
pp. 18-30 ◽  
Author(s):  
Masaki Hayashi ◽  
Kyoko Oshima ◽  
Masamoto Tanabiki ◽  
Yoshimitsu Aoki
Keyword(s):  
Pose Estimation ◽  
Team Sports ◽  
Lower Body ◽  
Tracking By Detection ◽  
Body Pose Estimation ◽  
Sports Videos

Evaluation of occupational exposure to static magnetic field in MRI sites based on body pose estimation and SMF analytical computation

2018 ◽  
Vol 39 (7) ◽  
pp. 503-515
Author(s):  
Lamia Belguerras ◽  
A.Rahim Kadkhodamohammadi ◽  
Antoine Delmas ◽  
Meysam Miralipoor ◽  
Nicolas Weber ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Occupational Exposure ◽  
Static Magnetic Field ◽  
Pose Estimation ◽  
Analytical Computation ◽  
Body Pose Estimation

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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