Abstract

Complex motion patterns, non-rigid deformations, occlusions, and illumination changes largely affect rotational image sequences. This makes accurate motion estimation a challenging task. To address this issue, we propose an optical flow model to accurately estimate motion in rotational image sequences. Our model uses an additional constraint in the objective weighted with an edge-stopping function which allows non-zero curl specifically in the regions where rotation is involved. Our implementation of the model relies upon the robust Chambolle-Pock algorithm. We discuss the effects of the model parameters and the primal-dual parameters in the convergence of the algorithm. To further validate the effectiveness of our model, we combine our algorithm with some of the sophisticated implementation approaches with weighted median filtering. Our results on some of the rotational sequences from the Middlebury benchmark datasets show that our method achieves the best average angular and end-point errors when compared with some of the well-known models in the literature.