Generating optical flow using NVIDIA flownet2-pytorch implementation

A guide into creating optical flow files which are to be used in video classification algorithms

This blog was originally published in blog.dancelogue.com. In a previous post, an introduction to optical flow was conducted, as well an overview of it’s architecture based on the FlowNet 2.o paper. This blog will focus in going deeper into optical flow, which will be done by generating optical flow files both from the standard Sintel data and a custom dance video. It will be conducted using a fork of the NVIDIA flownet2-pytorch code base which can be found in the Dancelogue linked repo.

The goal of this blog is to:

• Get the flownet2-pytorch codebase up and running.
• Download the relevant dataset as described by the example provided in the original repository.
• Generate optical flow files and then investigate the structure of the flow files.
• Convert the flow files into the color coding scheme to make them easier for humans to understand.
• Apply optical flow generation to dance videos and analyse the result.

System Requirements