Video Background Subtraction in Complex Environments

AbstractBackground subtraction models based on mixture of Gaussians have been extensively used for detecting objects in motion in a wide variety of computer vision applications. However, background subtraction modeling is still an open problem particularly in video scenes with drastic illumination changes and dynamic backgrounds (complex backgrounds). The purpose of the present work is focused on increasing the robustness of background subtraction models to complex environments. For this, we proposed the following enhancements: a) redefine the model distribution 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 dependent learning-rate parameter, and c) replace the pixel-based modeling currently used in the literature by a new space-time region-based model that eliminates the noise effect caused by drastic changes in illumination. Our proposed scheme can be implemented on any state of the art background subtraction scheme based on mixture of Gaussians to improve its resilient to complex backgrounds. Experimental results show excellent noise removal and object motion detection properties under complex environments