Datasets:
Modalities:
Image
Languages:
English
Size:
100K<n<1M
Tags:
human action recognition
skeleton-based human action recognition
joint skeletons
human interaction
cyber-physical-social systems
digital twins
License:
Update README.md
Browse files
README.md
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- [Action List](#action-list)
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- [Skeleton Features](#skeleton-features)
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- [Machine Learning Techniques](#machine-learning-techniques)
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- [Acknowledgements](#acknowledgements)
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- [Dataset Curators](#dataset-curation)
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- [Funding and Support](#funding-and-support)
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* Graph Neural Networks (GCN) : A convolutional model performed over a defined / specialized graph network as opposed to an array of pixels. A specific example of this is the Spatial-Temporal GCN (STGCN), which is popularly used among skeleton-based human action recognition.
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* Autoencoding : An unsupervised learning technique that can be used to learn sets of patterns and features shared by data. This can be particularly powerful for clustering data and quantifying differences between particular actions. This is also powerful in reducing data dimensionality - being able to represent the data using a smaller set of features than originally.
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## Acknowledgements
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- [Action List](#action-list)
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- [Skeleton Features](#skeleton-features)
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- [Machine Learning Techniques](#machine-learning-techniques)
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- [Caveat](#caveat)
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- [Acknowledgements](#acknowledgements)
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- [Dataset Curators](#dataset-curation)
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- [Funding and Support](#funding-and-support)
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* Graph Neural Networks (GCN) : A convolutional model performed over a defined / specialized graph network as opposed to an array of pixels. A specific example of this is the Spatial-Temporal GCN (STGCN), which is popularly used among skeleton-based human action recognition.
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* Autoencoding : An unsupervised learning technique that can be used to learn sets of patterns and features shared by data. This can be particularly powerful for clustering data and quantifying differences between particular actions. This is also powerful in reducing data dimensionality - being able to represent the data using a smaller set of features than originally.
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## Caveat
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Some users have experienced "Job manager crashed while running this job." owing to the size of the dataset. To solve this problem, it is recommended to download the dataset in batch.
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## Acknowledgements
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