Mosaic-based Figure-Ground Segmentation Along with Static Segmentation by Mean Shift

J. Sole, Y. Huang, and J. Llach (USA)


SIFT, RANSAC, mosaic, foreground extraction.


This work deals with the problem of background foreground segmentation in video scenes. We propose an approach that makes use of feature extraction and match ing, robust estimation, background mosaic generation, mo saic back-projection, and segmentation. We make contri butions in each of these steps. SIFT features are extracted from the video and then, features between consecutive frames are matched. There are mismatches as well as errors in the feature location. To surmount this, we use a robust approach that employs a modified version of the RANSAC algorithm and weighted total least squares. Knowing the global motion allows cre ating an initial foreground segmentation and the generation of a mosaic mainly using background data. The moving object extraction is done by combining a mean shift-based frame segmentation along with the information given by the error of the mosaic back-projection into each frame.

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