Hybrid activity and plan recognition for video streams. Granada, R., Pereira, R. F., Monteiro, J., Ruiz, D. D, Barros, R. C., & Meneguzzi, F. In Proceedings of the AAAI Workshop on Plan, Activity, and Intent Recognition (PAIR), 2017. 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 an environment. In this work, we address the problem of activity recognition in an indoor environment, focusing on a kitchen scenario. Unlike existing approaches that identify single actions from video sequences, we also identify the goal towards which the subject of the video is pursuing. Our hybrid approach combines a deep learning architecture to analyze raw video data and identify individual actions which are then processed by a goal recognition algorithm that uses a plan library describing possible overarching activities to identify the ultimate goal of the subject in the video. Experiments show that our approach achieves the state-of-the-art for identifying cooking activities in a kitchen scenario.
@inproceedings{granada2017hybrid,
title={Hybrid activity and plan recognition for video streams},
author={Granada, Roger and Pereira, Ramon Fraga and Monteiro, Juarez and Ruiz, Duncan D and Barros, Rodrigo Coelho and Meneguzzi, Felipe},
booktitle={Proceedings of the AAAI Workshop on Plan, Activity, and Intent Recognition (PAIR)},
year={2017},
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 an environment. In this work, we address the problem of activity recognition in an indoor environment, focusing on a kitchen scenario. Unlike existing approaches that identify single actions from video sequences, we also identify the goal towards which the subject of the video is pursuing. Our hybrid approach combines a deep learning architecture to analyze raw video data and identify individual actions which are then processed by a goal recognition algorithm that uses a plan library describing possible overarching activities to identify the ultimate goal of the subject in the video. Experiments show that our approach achieves the state-of-the-art for identifying cooking activities in a kitchen scenario.},
url = {files/pair-hybrid-recognizer-2017.pdf}
}
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