Background subtraction under sudden illumination change

In this paper, we propose a Multiple Background Model based Background Subtraction (MB2S) algorithm that is robust against sudden illumination changes in indoor environment. It uses multiple background models of expected illumination changes followed by both pixel and frame based background subtraction on both RGB and YCbCr color spaces. The masks generated after processing these input images are then combined in a framework to classify background and foreground pixels. Evaluation of proposed approach on publicly available test sequences show higher precision and recall than other state-of-the-art algorithms.