Markerless biomechanics in yearling racehorse selection - stage 1.

In addition to creating a deep learning neural network to understand what a 'good cardio' looks like I stumbled across a neat bit of work out of the Mathis Mouse Motor Lab at Harvard University. They have trained a deep learning neural network to automate the tracking of features on a mouse for varying tasks. Their paper, Markerless tracking of user-defined features with deep learning, is published on and the code for their work is on Github In laymans terms, what their work does is this..... Take a video of an animal and break the video down into frames. So if you have a 10 second video taken at 30 frames per second, you have 300 frames Markup each frame with a point on each biom

@2017 by Performance Genetics LLC