A Deep Neural Architecture for Kitchen Activity Recognition

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

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


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 publication2017 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE Press
ISBN (Electronic)978-1-5090-6182-2
Publication statusPublished - 2017

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.


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