Robust Background Subtraction based on Bi-polar Radial Reach Correlation

Background subtraction algorithms are widely utilized as a technology for segmentation of background and target objects in images. In particular, the simple background subtraction algorithm is used in many systems for its ease and low cost of implementation. However, because this algorithm relies only on the intensity difference, it has various problems, such as low tolerance for poor illumination and shadows and the inability to distinguish objects from their background when their intensities are similar. In an earlier study we proposed a new statistic, known as radial reach correlation (RRC), for distinguishing similar areas and dissimilar areas when comparing background images and target images at the pixel level. And we achieved a robust background subtraction by evaluating the local texture in images. In this study we extended this method further and developed a method to ensure stable background separation even in cases where the image texture is feeble and the intensity distribution is biased.