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Unsupervised action classification using space-time link analysis

Liu, Haowei ; Feris, Rogerio ; Krueger, Volker LU orcid and Sun, Ming Ting LU (2010) In Eurasip Journal on Image and Video Processing 2010. p.1-10
Abstract

We address the problem of unsupervised discovery of action classes in video data. Different from all existing methods thus far proposed for this task, we present a space-time link analysis approach which consistently matches or exceeds the performance of traditional unsupervised action categorization methods in various datasets. Our method is inspired by the recent success of link analysis techniques in the image domain. By applying these techniques in the space-time domain, we are able to naturally take into account the spatiotemporal relationships between the video features, while leveraging the power of graph matching for action classification. We present a comprehensive set of experiments demonstrating that our approach is capable... (More)

We address the problem of unsupervised discovery of action classes in video data. Different from all existing methods thus far proposed for this task, we present a space-time link analysis approach which consistently matches or exceeds the performance of traditional unsupervised action categorization methods in various datasets. Our method is inspired by the recent success of link analysis techniques in the image domain. By applying these techniques in the space-time domain, we are able to naturally take into account the spatiotemporal relationships between the video features, while leveraging the power of graph matching for action classification. We present a comprehensive set of experiments demonstrating that our approach is capable of handling cluttered backgrounds, activities with subtle movements, and video data from moving cameras. State-of-the-art results are reported on standard datasets. We also demonstrate our method in a compelling surveillance application with the goal of avoiding fraud in retail stores.

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Please use this url to cite or link to this publication:
author
; ; and
publishing date
type
Contribution to journal
publication status
published
in
Eurasip Journal on Image and Video Processing
volume
2010
article number
626324
pages
10 pages
publisher
Springer
external identifiers
  • scopus:77954610621
ISSN
1687-5176
DOI
10.1155/2010/626324
language
English
LU publication?
no
id
08450c06-6cb2-45ec-8965-f4c0e8e10e59
date added to LUP
2019-06-28 09:23:34
date last changed
2022-01-31 22:45:08
@article{08450c06-6cb2-45ec-8965-f4c0e8e10e59,
  abstract     = {{<p>We address the problem of unsupervised discovery of action classes in video data. Different from all existing methods thus far proposed for this task, we present a space-time link analysis approach which consistently matches or exceeds the performance of traditional unsupervised action categorization methods in various datasets. Our method is inspired by the recent success of link analysis techniques in the image domain. By applying these techniques in the space-time domain, we are able to naturally take into account the spatiotemporal relationships between the video features, while leveraging the power of graph matching for action classification. We present a comprehensive set of experiments demonstrating that our approach is capable of handling cluttered backgrounds, activities with subtle movements, and video data from moving cameras. State-of-the-art results are reported on standard datasets. We also demonstrate our method in a compelling surveillance application with the goal of avoiding fraud in retail stores.</p>}},
  author       = {{Liu, Haowei and Feris, Rogerio and Krueger, Volker and Sun, Ming Ting}},
  issn         = {{1687-5176}},
  language     = {{eng}},
  month        = {{07}},
  pages        = {{1--10}},
  publisher    = {{Springer}},
  series       = {{Eurasip Journal on Image and Video Processing}},
  title        = {{Unsupervised action classification using space-time link analysis}},
  url          = {{http://dx.doi.org/10.1155/2010/626324}},
  doi          = {{10.1155/2010/626324}},
  volume       = {{2010}},
  year         = {{2010}},
}