Reference : Pose Encoding for Robust Skeleton-Based Action Recognition
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/35566
Pose Encoding for Robust Skeleton-Based Action Recognition
English
Demisse, Girum mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Papadopoulos, Konstantinos mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Aouada, Djamila mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
18-Jun-2018
CVPRW: Visual Understanding of Humans in Crowd Scene, Salt Lake City, Utah, June 18-22, 2018
Yes
CVPRW: Visual Understanding of Humans in Crowd Scene
from 18-06-2018 to 22-06-2018
[en] Some of the main challenges in skeleton-based action recognition systems are redundant and noisy pose transformations. Earlier works in skeleton-based action recognition explored different approaches for filtering linear noise transformations, but neglect to address potential nonlinear
transformations. In this paper, we present an unsupervised learning approach for estimating nonlinear noise transformations in pose estimates. Our approach starts by decoupling linear and nonlinear noise transformations. While the linear transformations are modelled explicitly the nonlinear transformations are learned from data. Subsequently, we use an autoencoder with L2-norm reconstruction error and show that it indeed does capture nonlinear noise transformations,
and recover a denoised pose estimate which in turn improves performance significantly. We validate our approach on a publicly available dataset, NW-UCLA.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
http://hdl.handle.net/10993/35566
FnR ; FNR10415355 > Björn Ottersten > 3D-ACT > 3D Action Recognition Using Refinement and Invariance Strategies for Reliable Surveillance > 01/06/2016 > 31/05/2019 > 2015

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