TY - JOUR
T1 - Background modeling for foreground detection in real-world dynamic scenes
AU - Bouwmans, Thierry
AU - Gonzàlez, Jordi
AU - Shan, Caifeng
AU - Piccardi, Massimo
AU - Davis, Larry
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
U2 - 10.1007/s00138-013-0578-x
DO - 10.1007/s00138-013-0578-x
M3 - Review article
SN - 0932-8092
VL - 25
SP - 1101
EP - 1103
JO - Machine Vision and Applications (Q2:Computer Vision and Pattern Recognition)
JF - Machine Vision and Applications (Q2:Computer Vision and Pattern Recognition)
IS - 5
ER -