Efficient Background Subtraction under Abrupt Illumination Variations

Abstract

Background subtraction techniques require high segmentation quality and low computational cost. Achieving high accuracy is difficult under abrupt illumination changes. We develop a new background subtraction method in an expectation maximization (EM) framework. We describe foreground colors and illumination ratios using a few Gaussian mixture models. EM convergence is dependent on its initialization. We propose a novel initialization method that considers reflectance and illumination implicitly. Scene points occluded by a foreground object tend to have prominent illumination ratios since both the reflectance and illumination are different. We introduce a topological approach based on Morse theory to pre-classify pixels into foreground and background. Moreover, we only decompose the probability distributions in the initial step in our EM. Later iterations do not consider the probability distribution decomposition anymore. The experimental results demonstrate that our EM formulation provides high accuracy under abrupt variations in illumination. Additionally, in comparison with one of the state-of-the-art methods based on EM, our approach converges in fewer iterations, yielding computational savings.