Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness

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Presented by

  • Hudson Ash
  • Stephen Kingston
  • Richard Zhang
  • Alexandre Xiao
  • Ziqiu Zhu

Introduction

Optical flow is the pattern of apparent motion of image brightness patterns in objects, surfaces and edges in videos. In more laymen terms, it tracks the change in position between two frames caused by the movement of the object or the camera, and it does this on the basis of two assumptions:

1. Pixel intensities do not change rapidly between frames (brightness constancy).

2. Groups of pixels move together.

Both of these assumptions are derived from real-world implications. Firstly, the time between two consecutive frames of a video are so minuscule, such that it becomes extremely improbable for the intensity of a pixel to completely change, even if its location has changed. Secondly, pixels do not teleport. The assumption that groups of pixels move together implies that there is spacial coherence or smoothing between objects.

The current approach to solving optimal flow problems, albeit widely successful, has been a result of supervised learning methods.