A Deep Neural Architecture for Kitchen Activity Recognition

Computer-based human activity recognition of daily living has recently attracted much interest due to its applicability to ambient assisted living. Such applications require the automatic recognition of high-level activities composed of multiple actions performed by human beings in a given environment. We propose a deep neural architecture for kitchen activity recognition, which uses an ensemble of machine learning models and hand-crafted features to extract more information of the data. Experiments show that our approach achieves the state-of-the-art for identifying cooking actions in a well-known kitchen dataset.