Adaptive grid refinement procedures for efficient optical flow computation
Two approaches are described that improve the efficiency of optical flow computation without incurring loss of accuracy. The first approach segments images into regions of moving objects. The method is based on a previously defined Galerkin finite element method on a triangular mesh combined with a multiresolution segmentation approach for object flow computation. Images are automatically segmented into subdomains of moving objects by an algorithm that employs a hierarchy of mesh coarseness for the flow computation, and these subdomains are reconstructed over a finer mesh on which to recompute flow more accurately. The second approach uses an adaptive mesh in which the resolution increases where motion is found to occur. Optical flow is computed over a reasonably coarse mesh, and this is used to construct an optimal adaptive mesh in a way that is different from the gradient methods reported in the literature. The finite element mesh facilitates a reduction in computational effort by enabling processing to focus on particular objects of interest in a scene (i.e. those areas where motion is detected). The proposed methods were tested on real and synthetic image sequences, and promising results are reported.
adaptive grids optical flow triangular meshes delaunay algorithm inverse finite elements motion estimation