Article

Nonparametric motion model with applications to camera motion pattern classification

Authors:

Ling-Yu Duan,

Min Xu,

Qi Tian,

Chang-Sheng XuAuthors Info & Claims

MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia

Pages 328 - 331

https://doi.org/10.1145/1027527.1027603

Published: 10 October 2004 Publication History

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Abstract

Motion information is a powerful cue for visual perception. In the context of video indexing and retrieval, motion content serves as a useful source for compact video representation. There has been a lot of literature about parametric motion models. However, it is hard to secure a proper parametric assumption in a wide range of video scenarios. Diverse camera shots and frequent occurrences of bad optical flow estimation motivate us to develop nonparametric motion models. In this paper, we employ the mean shift procedure to propose a novel nonparametric motion representation. With this compact representation, various motion characterization tasks can be achieved by machine learning. Such a learning mechanism can not only capture the domain-independent parametric constraints, but also acquire the domain-dependent knowledge to tolerate the influence of bad dense optical flow vectors or block-based MPEG motion vector fields (MVF). The proposed nonparametric motion model has been applied to camera motion pattern classification on 23191 MVF extracted from MPEG-7 dataset.

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Cited By

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Abdollahian GTaskiran CPizlo ZDelp E(2010)Camera Motion-Based Analysis of User Generated VideoIEEE Transactions on Multimedia10.1109/TMM.2009.203628612:1(28-41)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1109/TMM.2009.2036286
Duan LJin JTian QXu C(2006)Nonparametric motion characterization for robust classification of camera motion patternsIEEE Transactions on Multimedia10.1109/TMM.2005.8643448:2(323-340)Online publication date: 1-Sep-2006
https://dl.acm.org/doi/10.1109/TMM.2005.864344
Tong XDuan LXu CTian QLu H(2006)Local Motion Analysis and Its Application in Video based Swimming Style RecognitionProceedings of the 18th International Conference on Pattern Recognition - Volume 0210.1109/ICPR.2006.770(1258-1261)Online publication date: 20-Aug-2006
https://dl.acm.org/doi/10.1109/ICPR.2006.770

Index Terms

Nonparametric motion model with applications to camera motion pattern classification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
      2. Image and video acquisition
        Motion capture
  2. Computer graphics
    1. Animation
      1. Motion capture
      2. Motion processing

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Published In

MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia

October 2004

1028 pages

ISBN:1581138938

DOI:10.1145/1027527

General Chairs:
Henning Schulzrinne
Columbia University
,
Nevenka Dimitrova
Philips Research
,
Program Chairs:
Angela Sasse
UCL
,
Sue Moon
KAIST
,
Rainer Lienhart
U Augsburg

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 October 2004

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MM04

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MM04: 2004 12th Annual ACM International Conference on Multimedia

October 10 - 16, 2004

NY, New York, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Abdollahian GTaskiran CPizlo ZDelp E(2010)Camera Motion-Based Analysis of User Generated VideoIEEE Transactions on Multimedia10.1109/TMM.2009.203628612:1(28-41)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1109/TMM.2009.2036286
Duan LJin JTian QXu C(2006)Nonparametric motion characterization for robust classification of camera motion patternsIEEE Transactions on Multimedia10.1109/TMM.2005.8643448:2(323-340)Online publication date: 1-Sep-2006
https://dl.acm.org/doi/10.1109/TMM.2005.864344
Tong XDuan LXu CTian QLu H(2006)Local Motion Analysis and Its Application in Video based Swimming Style RecognitionProceedings of the 18th International Conference on Pattern Recognition - Volume 0210.1109/ICPR.2006.770(1258-1261)Online publication date: 20-Aug-2006
https://dl.acm.org/doi/10.1109/ICPR.2006.770

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