Interactive Motion Segmentation
Interactive motion segmentation is an important task for scene understanding and analysis. Despite recent progress state-of-the-art approaches still have difficulties in adapting to the diversity of spatially varying motion fields. Due to strong, spatial variations of the motion field, objects are often divided into several parts. At the same time, different objects exhibiting similar motion fields often cannot be distinguished correctly. In this paper, we propose to use spatially varying affine motion model parameter distributions combined with minimal guidance via user drawn scribbles. Hence, adaptation to motion pattern variations and capturing subtle differences between similar regions is feasible. The idea is embedded in a variational minimization problem, which is solved by means of recently proposed convex relaxation techniques. For two regions (i.e. object and background) we obtain globally optimal results for this formulation. For more than two regions the results deviate within very small bounds of about 2 to 4 % from the optimal solution in our experiments. To demonstrate the benefit of using both model parameters and spatially variant distributions, we show results for challenging synthetic and real-world motion fields.
KeywordsMotion Vector Segmentation Result Motion Field Motion Segmentation Geodesic Active Contour
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