Learning a Confidence Measure for Optical Flow

  title={Learning a Confidence Measure for Optical Flow},
  author={Oisin Mac Aodha and Ahmad Humayun and Marc Pollefeys and Gabriel J. Brostow},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
We present a supervised learning-based method to estimate a per-pixel confidence for optical flow vectors. Regions of low texture and pixels close to occlusion boundaries are known to be difficult for optical flow algorithms. Using a spatiotemporal feature vector, we estimate if a flow algorithm is likely to fail in a given region. Our method is not restricted to any specific class of flow algorithm and does not make any scene specific assumptions. By automatically learning this confidence, we… CONTINUE READING
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