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Modeling long-term interactions to enhance action recognition

Author
Cartas, A.; Radeva, P.; Dimiccoli, M.
Type of activity
Presentation of work at congresses
Name of edition
25th International Conference on Pattern Recognition
Date of publication
2020
Presentation's date
2021
Book of congress proceedings
Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition: Milan, 10–15 January 2021
First page
10351
Last page
10358
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
10.1109/ICPR48806.2021.9412148
Repository
http://hdl.handle.net/2117/351241 Open in new window
URL
https://ieeexplore.ieee.org/document/9412148/ Open in new window
Abstract
In this paper, we propose a new approach to understand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical Long Short-T...
Citation
Cartas, A.; Radeva, P.; Dimiccoli, M. Modeling long-term interactions to enhance action recognition. A: International Conference on Pattern Recognition. "Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition: Milan, 10–15 January 2021". Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 10351-10358. ISBN 978-1-7281-8808-9. DOI 10.1109/ICPR48806.2021.9412148.
Keywords
Pattern recognition
Group of research
ROBiri - IRI Robotics Group

Participants

  • Cartas Ayala, Alejandro  (author and speaker )
  • Radeva, Petia  (author and speaker )
  • Dimiccoli, Maria  (author and speaker )