Imperial College London
Alternate

Professor Yiannis Demiris

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN
 
 
 
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Contact

 

+44 (0)20 7594 6300y.demiris Website

 
 
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Location

 

1011Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Buizza:2020,
author = {Buizza, C and Fischer, T and Demiris, Y},
publisher = {IEEE},
title = {Real-time multi-person pose tracking using data assimilation},
url = {http://openaccess.thecvf.com/content_WACV_2020/html/Buizza_Real-Time_Multi-Person_Pose_Tracking_using_Data_Assimilation_WACV_2020_paper.html},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We propose a framework for the integration of data assimilation and machine learning methods in human pose estimation, with the aim of enabling any pose estimation method to be run in real-time, whilst also increasing consistency and accuracy. Data assimilation and machine learning are complementary methods: the former allows us to make use of information about the underlying dynamics of a system but lacks the flexibility of a data-based model, which we can instead obtain with the latter. Our framework presents a real-time tracking module for any single or multi-person pose estimation system. Specifically, tracking is performed by a number of Kalman filters initiated for each new person appearing in a motion sequence. This permits tracking of multiple skeletons and reduces the frequency that computationally expensive pose estimation has to be run, enabling online pose tracking. The module tracks for N frames while the pose estimates are calculated for frame (N+1). This also results in increased consistency of person identification and reduced inaccuracies due to missing joint locations and inversion of left-and right-side joints.
AU  - Buizza,C
AU  - Fischer,T
AU  - Demiris,Y
PB  - IEEE
PY  - 2020///
TI  - Real-time multi-person pose tracking using data assimilation
UR  - http://openaccess.thecvf.com/content_WACV_2020/html/Buizza_Real-Time_Multi-Person_Pose_Tracking_using_Data_Assimilation_WACV_2020_paper.html
UR  - http://hdl.handle.net/10044/1/77561
ER  -
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