Deep neural networks for kitchen activity recognition

Juarez Monteiro, Roger Granada, Rodrigo C Barros, Felipe Meneguzzi

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

23 Citations (Scopus)


With the growth of video content produced by mobile cameras and surveillance systems, an increasing amount of data is becoming available and can be used for a variety of applications such as video surveillance, smart homes, smart cities, and in-home elder monitoring. Such applications focus in recognizing human activities in order to perform different tasks allowing the opportunity to support people in their different scenarios. In this paper we propose a deep neural architecture for kitchen human action recognition. This architecture contains an ensemble of convolutional neural networks connected through different fusion methods to predict the label of each action. Experiments show that our architecture achieves the novel state-of-the-art for identifying cooking actions in a well-known kitchen dataset.
Original languageEnglish
Title of host publicationNeural Networks (IJCNN), 2017 International Joint Conference on
PublisherIEEE Explore
Number of pages8
ISBN (Electronic)978-1-5090-6182-2
ISBN (Print)978-1-5090-6183-9
Publication statusPublished - 3 Jul 2017
Event2017 International Joint Conference on Neural Networks (IJCNN) - Anchorage, AK, USA, Anchorage, United States
Duration: 14 May 201717 May 2017


Conference2017 International Joint Conference on Neural Networks (IJCNN)
Country/TerritoryUnited States

Bibliographical note

This paper was achieved in cooperation with HP Brasil Indústria e Comércio de Equipamentos Eletrônicos LTDA. using incentives of Brazilian Informatics Law (Law № 8.2.48 of 1991). The authors also would like to thank FAPERGS, CNPq, and CAPES for funding this research.


  • neural networks
  • Computer architecture
  • Support vector machines
  • optical imaging
  • activity recognition
  • cameras


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