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Human Motion Parsing by Hierarchical Dynamic Clustering

2018

Conference Paper

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Parsing continuous human motion into meaningful segments plays an essential role in various applications. In this work, we propose a hierarchical dynamic clustering framework to derive action clusters from a sequence of local features in an unsuper- vised bottom-up manner. We systematically investigate the modules in this framework and particularly propose diverse temporal pooling schemes, in order to realize accurate temporal action localization. We demonstrate our method on two motion parsing tasks: temporal action segmentation and abnormal behavior detection. The experimental results indicate that the proposed framework is significantly more effective than the other related state-of-the-art methods on several datasets.

Author(s): Yan Zhang and Siyu Tang and He Sun and Heiko Neumann
Book Title: Proceedings of the British Machine Vision Conference (BMVC)
Pages: 269
Year: 2018
Month: September
Day: 3-6
Publisher: BMVA Press

Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Event Name: 29th British Machine Vision Conference
Event Place: Newcastle upon Tyne
Attachments: pdf

BibTex

@inproceedings{hdc:bmvc:2018,
  title = {Human Motion Parsing by Hierarchical Dynamic Clustering},
  author = {Zhang, Yan and Tang, Siyu and Sun, He and Neumann, Heiko},
  booktitle = {Proceedings of the British Machine Vision Conference (BMVC)},
  pages = {269},
  publisher = {BMVA Press},
  month = sep,
  year = {2018},
  month_numeric = {9}
}