{1412} revision 0 modified: 10-03-2018 23:56 gmt

Markerless tracking of user-defined features with deep learning

  • Human - level tracking with as few as 200 labeled frames.
  • No dynamics - could be even better with a Kalman filter.
  • Uses a Google-trained DCN, 50 or 101 layers deep.
    • Network has a distinct read-out layer per feature to localize the probability of a body part to a pixel location.
  • Uses the DeeperCut network architecture / algorithm for pose estimation.
  • These deep features were trained on ImageNet
  • Trained on examples with both only the readout layers (rest fixed per ResNet), as well as end-to-end; latter performs better, unsurprising.