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Here, a new and efficient strategy is introduced which allows moving objects detection and segmentation in video
sequences. Other strategies use the mixture of gaussians to detect static areas and dynamic areas within the images so
that moving objects are segmented [1], [2], [3], [4]. For this purpose, all these strategies use a fixed number of gaussians
per pixel. Typically, more than two or three gaussians are used to obtain good results when images contain noise and
movement not related to objects of interest. Nevertheless, the use of more than one gaussian per pixel involves a high
computational cost and, in many cases, it adds no advantages to single gaussian segmentation. This paper proposes a
novel automatic moving object segmentation which uses an adaptive variable number of gaussians to reduce the overall
computational cost. So, an automatic strategy is applied to each pixel to determine the minimum number of gaussians
required for its classification. Taking into account the temporal context that identifies the reference image pixels as
background (static) or moving (dynamic), either the full set of gaussians or just one gaussian are used. Pixels classified
with the full set are called MGP (Multiple Gaussian Pixel), while those classified with just one gaussian are called SGP
(Single Gaussian Pixel). So, a computation reduction is achieved that depends on the size of this last set. Pixels with a
dynamic reference are always MGP. They can be Dynamic-MGP (DMGP) when they belong to the dynamic areas of the
image. However, if the classification result shows that the pixel matches one of the gaussian set, then the pixel is labeled
static and therefore it is called Static-MGP (SMGP). Usually, these last ones are noise pixels, although they could belong
to areas with movement not related to objects of interest. Finally, pixels with a static reference that still match the same
gaussian are SGP and they belong to the static background of the image. However, if they do not match the associated
gaussian, they are changed either to SMGP or DMGP. In addition, any pixel can maintain its status and SMGP can be
changed to DMGP and SGP. A state diagram shows the transition schemes and its characterizations, allowing the
forecasting of the reduction of the computational cost of the segmentation process. Tests have shown that the use of the
proposed strategy implies a limited loss of accuracy in the segmentations obtained, when comparing with other strategies
that use a fixed number of gaussians per pixel, while achieving very high reductions of the overall computational cost of
the process.
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