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This paper presents the first method to compute dense scene flow in real-time for RGB-D cameras. It is based on a variational formulation where brightness constancy and geometric consistency are imposed. Accounting for the depth data provided by RGB-D cameras, regularization of the flow field is imposed on the 3D surface (or set of surfaces) of the observed scene instead of on the image plane, leading...
In this work we present a novel method for tracking an unknown number of objects with a single camera system in real-time. The proposed algorithm is based on high-accuracy optical flow and finite set statistics. In this framework the target state is treated as a random vector and the number of possible objects as a random number, which has to be estimated correctly. We are able to deal with false...
We propose an algorithm for large displacement optical flow estimation which does not require the commonly used coarse-to-fine warping strategy. It is based on a quadratic relaxation of the optical flow functional which decouples data term and regularizer in such a way that the non-linearized variational problem can be solved by an alternation of two globally optimal steps, one imposing optimal data...
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