Using scene context to improve action recognition

Juarez Monteiro*, Roger Granada, Felipe Meneguzzi, Rodrigo Coelho Barros

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution


Recently action recognition has been used for a variety of applications such as surveillance, smart homes, and in-home elder monitoring. Such applications usually focus on recognizing human actions without taking into account the different scenarios where the action occurs. In this paper, we propose a two-stream architecture that considers not only the movements to identify the action, but also the context scene where the action is performed. Experiments show that the scene context may improve the recognition of certain actions. Our proposed architecture is tested against baselines and the standard two-stream network.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 23rd Iberoamerican Congress, CIARP 2018, Proceedings
EditorsRuben Vera-Rodriguez, Julian Fierrez, Aythami Morales
Number of pages8
ISBN (Print)9783030134686
Publication statusPublished - 2019
Event23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018 - Madrid, Spain
Duration: 19 Nov 201822 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11401 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018

Bibliographical note

Funding Information:
Acknowledgement. The authors would like to thank CAPES/FAPERGS and Motorola Mobility for partially funding this research. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

Publisher Copyright:
© Springer Nature Switzerland AG 2019.


  • Action recognition
  • Convolutional Neural Networks
  • Neural networks


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