Improved background modeling of video sequences using spatio-temporal extension of fuzzy local binary pattern

Background subtraction is a method of motion detection in video sequences captured by static camera based on construction of a background model and its progressive comparison with each frame of the video. Sometimes the changes in the background objects are not permanent and appear at a rate faster than that of the background update, and this leads to emergence of dynamic textures in the background. As a result, high-quality background modeling plays a major role in motion detection performance, especially in videos with dynamic backgrounds and adverse environmental conditions such as noise. Although Local Binary Pattern (LBP) is a successful methodology for background subtraction, but it cannot properly extract textures from uniform areas of the foreground. In recent years, Fuzzy Local Binary Pattern (FLBP) has been developed to improve the performance of LBP operator in texture extraction from images with additive noise. The use of FLBP texture descriptor for background subtraction has led to improved robustness against noise and low sensitivity of the background model to slight changes in the texture gray scale values, and therefore better texture extraction from uniform areas, even in the presence of dynamic backgrounds and adverse environmental conditions. But despite this improvement, this operator is still sensitive to time-variations of pixels in dynamic backgrounds. To avoid this issue and incorporate correlation of pixel values over successive frames, this study proposes the use of spatio-temporal extension of FLBP with symmetry about central pixel in combination with the Local Color Histogram (LCH) in the Improved Hue Luminance and Saturation (IHLS) color space for describing the pixel color features. The results of tests conducted with standard databases show that for dynamic backgrounds, the use of proposed Spatio-Temporal Fuzzy Center Symmetric Local Binary Pattern (STFCS-LBP) operator with the spatio-temporal neighborhood texture patterns and the local color histogram yields better results than the existing methods.