Adaptive foreground object extraction for real-time video surveillance with lighting variations

In this paper we present an adaptive foreground object extraction algorithm for real-time video surveillance. The proposed algorithm improves the previous Gaussian mixture background models (GMMs) by applying a two-stage foreground/background classification procedure to remove the undesirable subtraction results due to shadow, automatic white balance, and sudden illumination change. The traditional background subtraction technique usually cannot work well for situations with lighting variations in the scene. In the proposed two-stage classification, an adaptive classifier is applied to the foreground pixels in a pixel-wise manner based on the normalized color and brightness gain information. Secondly, the remaining foreground candidate pixels are grouped into regions and the corresponding background regions are compared to check if they are foreground regions. Experimental results on some real surveillance video are shown to demonstrate the robustness of the proposed adaptive foreground extraction algorithm under a variety of different environments with lighting variations.