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Foreground detection based on motion vector from compressed video

Published: 29 July 2008 Publication History

Abstract

A new algorithm to distinguish the foreground from compressed videos is proposed in this paper. Local motion, which is estimated from the residual between the original motion and the global motion, is one of the strongest influences on visual attention. Global motion is modeled by four parameters related to camera pan, tilt, zoom and rotation. The initial parameters are obtained from the least-squares method and updated iteratively using the Levenberg-Marquardt algorithm. Temporal and spatial filters are also introduced to revise the final global motion. In addition, DC coefficients are employed to refine the result based on the local motion. Experiments show that the proposed algorithm can segment foreground effectively with a largely reduced computational complexity, as DC coefficients and motion vectors can easily be extracted from compressed videos.

References

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cover image ACM Other conferences
ICAIT '08: Proceedings of the 2008 International Conference on Advanced Infocomm Technology
July 2008
677 pages
ISBN:9781605580883
DOI:10.1145/1509315
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 July 2008

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Author Tags

  1. DC coefficient
  2. compressed video
  3. foreground detection
  4. object motion
  5. temporal and spatial filter

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ICAIT '08 Paper Acceptance Rate 89 of 151 submissions, 59%;
Overall Acceptance Rate 122 of 207 submissions, 59%

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