Robust Background Modeling and Foreground Detection using Dynamic Textures

@inproceedings{Zitouni2016RobustBM,
  title={Robust Background Modeling and Foreground Detection using Dynamic Textures},
  author={M. Sami Zitouni and Harish Bhaskar and Mohammed E. Al-Mualla},
  booktitle={VISIGRAPP},
  year={2016}
}
In this paper, a dynamic background modeling and hence foreground detection technique using a Gaussian Mixture Model (GMM) of spatio-temporal patches of dynamic texture (DT) is proposed. Existing methods for background modeling cannot adequately distinguish movements in both background and foreground, that usually characterizes any dynamic scene. Therefore, in most of these methods, the separation of the background from foreground requires precise tuning of parameters or an apriori model of the… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 19 REFERENCES

Joint Motion Segmentation and Background Estimation in Dynamic Scenes

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition
  • 2014
VIEW 27 EXCERPTS
HIGHLY INFLUENTIAL

Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2011
VIEW 21 EXCERPTS
HIGHLY INFLUENTIAL

Layered Dynamic Textures

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2005
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Adaptive background mixture models for real-time tracking

  • Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)
  • 1999
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

PETS2009: Dataset and challenge

  • 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance
  • 2009
VIEW 2 EXCERPTS
HIGHLY INFLUENTIAL

Changedetection.net: A new change detection benchmark dataset

  • 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
  • 2012
VIEW 1 EXCERPT

Multl-resolution background subtraction for dynamic scenes

  • 2009 16th IEEE International Conference on Image Processing (ICIP)
  • 2009
VIEW 2 EXCERPTS

Spatial-temporal nonparametric background subtraction in dynamic scenes

  • 2009 IEEE International Conference on Multimedia and Expo
  • 2009
VIEW 3 EXCERPTS