Hierarchical coherency sensitive hashing and interpolation with RANSAC for large displacement optical flow

摘要

Nearest Neighbor Field (NNF) has shown excellent performance for large displacement optical flow estimation recently. However, it contains much noise and lacks of global constraint. In this paper we present an effective approach, named HCSH (Hierarchical Coherency Sensitive Hashing), which combines the coarse-to-fine scheme and random search strategy, to enable NNF to enjoy the inherent smooth of coarse-to-fine framework. Then besides the forward–backward check for NNF, we also use auto-correlation to remove the unreliable correspondences in flat regions, where the NNF is often considered ambiguous and the motion can be naturally recovered by the latter interpolation. Inspired by EpicFlow, we propose edge-aware interpolation (EAI-Flow) to filling the gaps by removing correspondence. RANSAC is introduced to improve the locality-weighted affine transformation estimation, with neighbor propagation of affine model to reduce required iterations and speed up the computation. Experimental validation shows that our approach outperforms the state-of-the-art with more accurate optical flow.