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.
|Title of host publication||2017 International Joint Conference on Neural Networks (IJCNN)|
|Publication status||Published - 2017|