Occlusion Detection and Motion Estimation with Convex Optimization

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

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Alper Ayvaci, Michalis Raptis, Stefano Soatto


We tackle the problem of simultaneously detecting occlusions and estimating optical flow. We show that, under standard assumptions of Lambertian reflection and static illumination, the task can be posed as a convex minimization problem. Therefore, the solution, computed using efficient algorithms, is guaranteed to be globally optimal, for any number of independently moving objects, and any number of occlusion layers. We test the proposed algorithm on benchmark datasets, expanded to enable evaluation of occlusion detection performance.