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Local Optical Flow Considering Object Boundaries by Adaptive Window Positioning

The amount of applications based on motion information has been growing rapidly, whereas accurate motion estimation is essential. Local optical flow methods, being typically based on the method of Lucas and Kanade, estimate the motion independently for each desired image position. Hereby, the mean squared error is minimized for a window symmetrically arranged around the considered position assuming constant motion within the whole window. However, this assumption is violated if the window contains objects of different motions. Furthermore, at object boundaries occlusions and disclosures may occur within the window. Consequently, large estimation errors are the result. In this paper, it is investigated if these disturbing influences can be reduced by windows, which are adaptively arranged avoiding object boundaries within them. This technique reduces the average absolute motion estimation error from 0.628 to 0.354 picture elements for the Middlebury and from 7.98 to 2.97 picture elements for the MPI Sintel dataset.