Abstract
Existing methods for optical flow estimation can perform well on images with small offsets of large objects. However, they could fail when there are a number of small and fast-moving objects. In particular, current deep learning methods often impose a down-sampling of the input data for a reduction of computational complexity. This practice inevitably incurs a loss of image details and causes small objects be ignored. To solve the problem, a multi-scale attention-aware network (MA-Net) is proposed, in which coarse-scale and fine-scale features are extracted in parallel and then attention is paid to them for producing the optical flow estimation. Extensive experiments conducted on the Sintel and KITTI2015 datasets show that the proposed MA-Net can capture fast-moving small objects with high accuracy and thus deliver superior performance over a number of state-of-the-art methods.
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