Segmenting foreground objects from highly dynamic scenes with missing data is very challenging. We present a novel unsupervised segmentation approach that can cope with extensive scene dynamic as well as a substantial amount of missing data that present in dynamic scene. To make this possible, we exploit convex optimization of total variation beforehand for images with missing data in which depletion mask is available. Inpainting depleted images using total variation facilitates detecting ambiguous objects from highly dynamic images, because it is more likely to yield areas of object instances with improved grayscale contrast. We use a conditional random field that adapts to integrate both appearance and motion knowledge of the foreground objects. Our approach segments foreground object instances while inpainting the highly dynamic scene with a variety amount of missing data in a coupled way. We demonstrate this on a very challenging dataset from the UCSD Highly Dynamic Scene Benchmarks (HDSB) and compare our method with two state-of-the-art unsupervised image sequence segmentation algorithms and provide quantitative and qualitative performance comparisons.
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