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Occlusion Detection and Motion Estimation with Convex Optimization

Conference Paper Artificial Intelligence ยท Machine Learning

Abstract

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.

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Context

Venue
Annual Conference on Neural Information Processing Systems
Archive span
1987-2025
Indexed papers
30776
Paper id
116867819300181606
v2026.09.13