Sparse Flow: Sparse Matching for Small to Large Displacement Optical Flow
Radu Timofte, Luc Van Gool · 2015
Despite recent advances, the extraction of optical flow with large displacements is still challenging for state-of the-art methods. The approaches that are the most successful at handling large displacements blend sparse correspondences from a matching algorithm with an optimization that refines the optical flow. We follow the scheme of Deep-Flow [33]. We first extract sparse pixel correspondences by means of a matching procedure and then apply a variational approach to obtain a refined optical flow. In our approach, coined 'Sparse Flow', the novelty lies in the matching. This uses an efficient sparse decomposition of a pixel's surrounding patch as a linear sum of those found around candidate corresponding pixels. As matching pixel the one dominating the decomposition is chosen. The pixel pairs matching in both directions, i.e. in a forward-backward fashion, are used as guiding points in the variational approach. Sparse-Flow is competitive on standard optical flow benchmarks with large displacements, while showing excellent performance for small and medium displacements. Moreover, it is fast in comparison to methods with a similar performance.