Reference : A Revisit of Action Detection using Improved Trajectories
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/35012
A Revisit of Action Detection using Improved Trajectories
English
Papadopoulos, Konstantinos mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Antunes, Michel mailto []
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) > >]
2018
IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Alberta, Canada, 15–20 April 2018
Yes
International Conference on Acoustics, Speech and Signal Processing
from 15-04-2018 to 20-04-2018
IEEE
Calgary, Alberta
Canada
[en] action detection ; improved trajectories ; action proposals
[en] In this paper, we revisit trajectory-based action detection in a potent and non-uniform way. Improved trajectories have been proven to be an effective model for motion description in action recognition. In temporal action localization, however, this approach is not efficiently exploited. Trajectory features extracted from uniform video segments result in significant performance degradation due to two reasons: (a) during uniform segmentation, a significant amount of noise is often added to the main action and (b) partial actions can have negative impact in classifier's performance. Since uniform video segmentation seems to be insufficient for this task, we propose a two-step supervised non-uniform segmentation, performed in an online manner. Action proposals are generated using either 2D or 3D data, therefore action classification can be directly performed on them using the standard improved trajectories approach. We experimentally compare our method with other approaches and we show improved performance on a challenging online action detection dataset.
Fonds National de la Recherche - FnR
http://hdl.handle.net/10993/35012
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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