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Temporal Human Action Segmentation via Dynamic Clustering

2018

Article

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We present an effective dynamic clustering algorithm for the task of temporal human action segmentation, which has comprehensive applications such as robotics, motion analysis, and patient monitoring. Our proposed algorithm is unsupervised, fast, generic to process various types of features, and applica- ble in both the online and offline settings. We perform extensive experiments of processing data streams, and show that our algorithm achieves the state-of- the-art results for both online and offline settings.

Author(s): Yan Zhang and He Sun and Siyu Tang and Heiko Neumann
Journal: arXiv preprint arXiv:1803.05790
Year: 2018

Department(s): Perceiving Systems
Bibtex Type: Article (article)
Paper Type: Journal

Institution: arXiv
URL: https://arxiv.org/pdf/1803.05790.pdf

BibTex

@article{humanActionSegmentationZhang,
  title = {Temporal Human Action Segmentation via Dynamic Clustering},
  author = {Zhang, Yan and Sun, He and Tang, Siyu and Neumann, Heiko},
  journal = {arXiv preprint 	arXiv:1803.05790},
  institution = {arXiv},
  year = {2018},
  url = {https://arxiv.org/pdf/1803.05790.pdf}
}