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Unsupervised open-world human action recognition

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Abstract

Open-world recognition (OWR) is an important field of research that strives to develop machine learning models capable of identifying and learning new classes as they appear. Concurrently, human action recognition (HAR) has received increasing attention from the research community. We approach Open-World HAR in the unsupervised setting. In unsupervised OWR, class labels are available for the initial classes but not for new ones. Hence, we propose a clustering method to label unknown classes automatically for incremental learning (IL). Our framework consists of an Initial Learning phase for initializing the models, an open-set recognition phase for identifying unknown classes, an Automatic Clustering phase for estimating the number of clusters and generating labels, and an IL phase for incorporating new knowledge. The proposed framework was evaluated at each phase separately in eleven experimental settings of the UCF-101 dataset. We also presented parameter sensitivity studies of the main parameters and visual analysis of misclassified videos, revealing interesting visual similarities between overlapped classes. Experiments have shown promising results in all phases of Open-World HAR, even without labels, which closely resembles real-world problems.

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Data availability

The UCF-101 dataset [86] that support the findings of this study are available from the University of Central Florida (UCF) website, [https://www.crcv.ucf.edu/data/UCF101.php].

Notes

  1. https://github.com/matheusgutoski/unsupervised-openworld-video-classification.

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Acknowledgements

Author M. Gutoski would like to thank CNPq for the scholarship number 141983/2018-3. Author H. S. Lopes would like to thank to CNPq for the research grant 311785/2019- 0, and Fundação Araucária for grant PRONEX 042/2018. Author A. E. Lazzaretti would like to thank to CNPq for the research grant 306569/2022-1. All authors would like to thank NVIDIA Corp. for the donation of the Titan-Xp GPUs used in the experiments.

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  1. Electrial Engineering and Industrial Informatics, Federal University of Technology - Paraná, Sete de Setembro, 3165 - Rebouças, Curitiba, Paraná, 80230-901, Brazil

    Matheus Gutoski, André Eugenio Lazzaretti & Heitor Silvério Lopes

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Gutoski, M., Lazzaretti, A.E. & Lopes, H.S. Unsupervised open-world human action recognition. Pattern Anal Applic 26, 1753–1770 (2023). https://doi.org/10.1007/s10044-023-01202-7

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