ProFlow: Learning to Predict Optical Flow

Understand

Temporal coherence is a valuable source of information in the context of optical flow estimation.

• However, finding a suitable motion model to leverage this information is a non-trivial task.
• In this paper we propose an unsupervised online learning approach based on a convolutional neural network (CNN) that estimates such a motion model individually for each frame.
• By relating forward and backward motion these learned models not only allow to infer valuable motion information based on the backward flow, they also help to improve the performance at occlusions, where a reliable prediction is particularly useful.