Robust Foreground Object Segmentation via Adaptive Region-Based Background Modelling

Abstract

We propose a region-based foreground object segmentation method capable of dealing with image sequences containing noise, illumination variations and dynamic backgrounds (as often present in outdoor environments). The method utilises contextual spatial information through analysing each frame on an overlapping block by-block basis and obtaining a low-dimensional texture descriptor for each block. Each descriptor is passed through an adaptive multi-stage classifier, comprised of a likelihood evaluation, an illumination invariant measure, and a temporal correlation check. The overlapping of blocks not only ensures smooth contours of the foreground objects but also effectively minimises the number of false positives in the generated foreground masks. The parameter settings are robust against wide variety of sequences and post-processing of foreground masks is not required. Experiments on the challenging I ² R dataset show that the proposed method obtains considerably better results (both qualitatively and quantitatively) than methods based on Gaussian mixture models (GMMs), feature histograms, and normalised vector distances. On average, the proposed method achieves 36% more accurate foreground masks than the GMM based method.