A new strategy based on adaptive mixture of Gaussians for real-time moving objects segmentation

Abstract

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.