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The ability to identify the static background in videos captured by a movingcamera is an important pre-requisite for many video applications (e.g. videostabilization, stitching, and segmentation). Existing methods usually facedifficulties when the foreground objects occupy a larger area than thebackground in the image. Many methods also cannot scale up to handle denselysampled feature trajectories. In this paper, we propose an efficientlocal-to-global method to identify background, based on the assumption that aslong as there is sufficient camera motion, the cumulative background featureswill have the largest amount of trajectories. Our motion model at the two-framelevel is based on the epipolar geometry so that there will be noover-segmentation problem, another issue that plagues the 2D motionsegmentation approach. Foreground objects erroneously labelled due tointermittent motions are also taken care of by checking their globalconsistency with the final estimated background motion. Lastly, by virtue ofits efficiency, our method can deal with densely sampled trajectories. Itoutperforms several state-of-the-art motion segmentation methods on publicdatasets, both quantitatively and qualitatively.

Robust Video Background Identification by Dominant Rigid Motion Estimation