any video surveillance system. GMM (Gaussian Mixture Models) based object segmentation

methods give accurate results for background-foreground separation problems but are

computationally expensive. In contrast, modeling with only single Gaussian improves the

time complexity with the reduction in the accuracy due to variations in illumination and

dynamic nature of the background. It is observed that these variations affect only a few

pixels in an image. Most of the background pixels are unimodal. We propose a method

to account for the dynamic nature of the background and low lighting conditions. It is an

adaptive approach where each pixel is modeled as either unimodal Gaussian or multimodal

Gaussians. The flexibility in terms of number of Gaussians used to model each pixel, along

with learning when it is required approach reduces the time complexity of the algorithm

significantly. To resolve problems related to false negative due to the homogeneity of color

and texture in foreground and background, a spatial smoothing is carried out by K-means,

which improves the overall accuracy of proposed algorithm. The shadow causes the problem

in many applications which rely on segmentation results. Shadow cause variation in

RGB values of pixels, RGB value dependent GMM based method can’t remove shadow

from detection results. The preprocessing stage involving illumination invariant representation

takes care of the object shadow as well.