Robust fast extraction of video objects combining frame differences and adaptive reference image

This paper introduces a video object segmentation algorithm developed in the context of the European project Art.live where constraints on the quality of segmentation and the processing rate (at least 10 images/second) are required. In order to obtain a fine segmentation (no blocking effect, boundaries precision, temporal stability without flickering), the segmentation process is based on Markov random field (MRF) modelling which involves consecutive frame difference and a reference image in a unified way. Temporal changes of the luminance are predominant when the reference image is not yet available whereas the reference image prevails for low textured moving objects or for objects which stop moving for a while. The increased processing rate comes from the substitution of some Markovian iterations with morphological operations without loss of quality. Simulation results show the efficiency of the proposed method in term of accuracy and complexity (≃6 images/second for 352×288 pixels YUV images on a low-end processor)