Computing occluding and transparent motions
International Journal of Computer Vision
Matrix computations (3rd ed.)
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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This work deals with the problem of background-foreground segmentation in video scenes. We propose an approach that makes use of feature extraction and matching, robust estimation, background mosaic generation, mosaic back-projection, and segmentation. We make contributions 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 creating 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.