Unsupervised convolutional neural networks for motion estimation

Understand

Traditional methods for motion estimation estimate the motion field F between a pair of images as the one that minimizes a predesigned cost function.

• In this paper, we propose a direct method and train a Convolutional Neural Network (CNN) that when, at test time, is given a pair of images as input it produces a dense motion field F at its output layer.
• In the absence of large datasets with ground truth motion that would allow classical supervised training, we propose to train the network in an unsupervised manner.
• The proposed cost function that is optimized during training, is based on the classical optical flow constraint.