Special issue on background modeling for foreground detection in real-world dynamic scenes
- Publication Type:
- Journal Article
- Citation:
- Machine Vision and Applications, 2014, 25 (5), pp. 1101 - 1103
- Issue Date:
- 2014-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| art%3A10.1007%2Fs00138-013-0578-x.pdf | Published Version | 98.66 kB | |||
| Message from the Editor in Chief.txt | Accepted Manuscript Version | 19.38 kB |
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The 2014 Special Issue of Machine Vision and Applications discuss papers on the background modeling for foreground detection in real-world dynamic scenes. Shah and co-researchers adopt the mixture of Gaussians (MOG) as the basic framework for their complete system. A new online and self-adaptive method permits an automatic selection of the parameters for the GMM. Shimada and co-researchers propose a novel framework for the GMM to reduce the memory requirement without loss of accuracy. This 'case-based background modeling' creates or removes a background model only when necessary. Alvar and co-researchers present an algorithm called mixture of merged Gaussian algorithm (MMGA) to reduce drastically the execution time to reach real-time implementation, without altering the reliability and accuracy. Hagege describes a scene appearance model as a function of the behavior of static illumination sources, within or beyond the scene, and arbitrary three-dimensional configurations of patches and their reflectance distributions.
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