Reference : Two-stage RGB-based Action Detection using Augmented 3D Poses
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/39789
Two-stage RGB-based Action Detection using Augmented 3D Poses
English
Papadopoulos, Konstantinos mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ghorbel, Enjie mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Baptista, Renato 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) > >]
2019
18th International Conference on Computer Analysis of Images and Patterns SALERNO, 3-5 SEPTEMBER, 2019
Yes
18th International Conference on Computer Analysis of Images and Patterns
from 03-09-2019 to 05-09-2019
[en] Action detection ; LSTM ; pose estimation ; action proposals
[en] In this paper, a novel approach for action detection from RGB sequences is proposed. This concept takes advantage of the recent development of CNNs to estimate 3D human poses from a monocular camera. To show the validity of our method, we propose a 3D skeleton-based two-stage action detection approach. For localizing actions in unsegmented sequences, Relative Joint Position (RJP) and Histogram Of Displacements (HOD) are used as inputs to a k-nearest neighbor binary classifier in order to define action segments. Afterwards, to recognize the localized action proposals, a compact Long Short-Term Memory (LSTM) network with a de-noising expansion unit is employed. Compared to previous RGB-based methods, our approach offers robustness to radial motion, view-invariance and low computational complexity. Results on the Online Action Detection dataset show that our method outperforms earlier RGB-based approaches.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Fonds National de la Recherche - FnR
http://hdl.handle.net/10993/39789
FnR ; FNR10415355 > Bjorn 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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