In this section
@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}
}
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 -
