Video Background Subtraction in Complex Environments

Abstract

Background subtraction models based on mixture of Gaussians have been extensively used for detecting objects inmotion in a wide variety of computer vision applications. However, background subtraction modeling is still an openproblem particularly in video scenes with drastic illumination changes and dynamic backgrounds (complexbackgrounds). The purpose of the present work is focused on increasing the robustness of background subtractionmodels to complex environments. For this, we proposed the following enhancements: a) redefine the modeldistribution parameters involved in the detection of moving objects (distribution weight, mean and variance), b)improve pixel classification (background/foreground) and variable update mechanism by a new time-space dependentlearning-rate parameter, and c) replace the pixel-based modeling currently used in the literature by a new space-timeregion-based model that eliminates the noise effect caused by drastic changes in illumination. Our proposed schemecan be implemented on any state of the art background subtraction scheme based on mixture of Gaussians toimprove its resilient to complex backgrounds. Experimental results show excellent noise removal and object motiondetection properties under complex environments.