A Deep Neural Architecture for Kitchen Activity Recognition. Granada, R., Monteiro, J., Barros, R. C., & Meneguzzi, F. In The Florida Artificial Intelligence Research Society Conference (FLAIRS), 2017.
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Paper abstract bibtex
Paper abstract bibtex 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 wellknown kitchen dataset.
@inproceedings{granada2017deep,
title={A Deep Neural Architecture for Kitchen Activity Recognition},
author={Granada, Roger and Monteiro, Juarez and Barros, Rodrigo Coelho and Meneguzzi, Felipe},
year={2017},
booktitle={The Florida Artificial Intelligence Research Society Conference (FLAIRS)},
abstract = {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 wellknown kitchen dataset.},
url = {files/deep_neural_networks_for_kitchen_flairs.pdf}
}Downloads: 0
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