The paper addresses the problem of robust optical flow computation for real-time applications. We present an efficient, in presence of varying illumination and large displacements, method of dense optical flow calculation based on Robust Local Optical Flow (RLOF). Introduction of Adaptive and Generic Accelerated Segment Test (AGAST) feature detector with additional augmentation by simple grid points provides a stable and reliable under difficult conditions set of features and uniform points that produces dense and accurate optical flow and leads to better performance. The usage of feature points for prior initialization leads for robustness for large displacements as well. The other essential step of optical flow calculation – densification of optical flow – was strengthen by modification of weights. The raster interpolation was applied with weights on second derivatives to make more edgesensitive optical flow. To enhance the overall result, a post-processing step of variational refinement process were added to optical flow calculation framework. The proposed method was evaluated on Max Planck Institute (MPI) Sintel dataset – a challenging set of 3D-animated movies with large range of motions and illuminations, which has ground truth optical flow available. The comparison with initial RLOF method were measured by running time and quality parameters, measured by End Point Error (EPE) and Angular Error (AE). The evaluation shows the decrease of running time by approximately 40 % and improvement of EPE and AE, that demonstrates the effectiveness of the method.
