TY - JOUR
T1 - Improved Gaussian Mixture Models for Adaptive Foreground Segmentation
AU - Katsarakis, Nikolaos
AU - Pnevmatikakis, Aristodemos
AU - Tan, Zheng-Hua
AU - Prasad, Ramjee
PY - 2016/4
Y1 - 2016/4
N2 - Adaptive foreground segmentation is traditionally performed using
Stauffer & Grimson’s algorithm that models every pixel of the frame by a
mixture of Gaussian distributions with continuously adapted parameters. In
this paper we provide an enhancement of the algorithm by adding two important
dynamic elements to the baseline algorithm: The learning rate can
change across space and time, while the Gaussian distributions can be merged
together if they become similar due to their adaptation process. We quantify
the importance of our enhancements and the effect of parameter tuning using
an annotated outdoors sequence.
AB - Adaptive foreground segmentation is traditionally performed using
Stauffer & Grimson’s algorithm that models every pixel of the frame by a
mixture of Gaussian distributions with continuously adapted parameters. In
this paper we provide an enhancement of the algorithm by adding two important
dynamic elements to the baseline algorithm: The learning rate can
change across space and time, while the Gaussian distributions can be merged
together if they become similar due to their adaptation process. We quantify
the importance of our enhancements and the effect of parameter tuning using
an annotated outdoors sequence.
KW - Adaptive foreground segmentation
KW - Adaptive background mixture models
KW - Gaussian Mixture Models
U2 - 10.1007/s11277-015-2628-3
DO - 10.1007/s11277-015-2628-3
M3 - Journal article
SN - 0929-6212
VL - 87
SP - 629
EP - 643
JO - Wireless Personal Communications
JF - Wireless Personal Communications
IS - 3
ER -