/dyn_pose

Dynamic pose estimation in MXNet. faster-rcnn detection + cpm pose estimation + LSTM dynamical modeling

Primary LanguageJupyter NotebookMIT LicenseMIT

dyn_pose

Training and inference code for LSTM-based action recognition model. Online version to be released later.

Steps

  • models
  • pre-process data: preprocess.py / split.py
  • extract pose: extract.py
  • train model: train_lstm.py
  • evaluate model: infer_lstm.py
  • offline demo: example.py

Notes

  • modify label_num in model/lstm.config

Utils

  • display.ipynb: display pose estimation results
  • run.sh: evaluation script
  • test.ipynb: miscellaneous

TODO

  • reduce spatial net size

Reference