Reference : Localized Trajectories for 2D and 3D Action Recognition
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/40332
Localized Trajectories for 2D and 3D Action Recognition
English
Papadopoulos, Konstantinos mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Demisse, Girum [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) > >]
Antunes, Michel []
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
Sensors
Multidisciplinary Digital Publishing Institute (MDPI)
Yes
International
1424-8220
1424-3210
Basel
Switzerland
[en] action recognition ; Dense Trajectories ; Local Bag-of-Words ; spatiotemporal features
[en] The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion. However, due to noise and background motion, many generated trajectories are irrelevant to the actual human activity and can potentially lead to performance degradation. In this paper, we propose Localized Trajectories as an improved version of Dense Trajectories where motion trajectories are clustered around human body joints provided by RGB-D cameras and then encoded by local Bag-of-Words. As a result, the Localized Trajectories concept provides an advanced discriminative representation of actions. Moreover, we generalize Localized Trajectories to 3D by using the depth modality. One of the main advantages of 3D Localized Trajectories is that they describe radial displacements that are perpendicular to the image plane. Extensive experiments and analysis were carried out on five different datasets.
http://hdl.handle.net/10993/40332
10.3390/s19163503
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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