GCN_LogsigRNN

This repository holds the codebase for the paper: Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition Shujian Liao, Terry Lyons, Weixin Yang, Kevin Schlegel, and Hao Ni, BMVC 2021

Data Preprocessing

Directory Structure

Put downloaded data into the following directory structure:

- data/
  - nturgbd_raw/
    - nturgb+d_skeletons/     # from `nturgbd_skeletons_s001_to_s017.zip`
      ...
    - nturgb+d_skeletons120/  # from `nturgbd_skeletons_s018_to_s032.zip`
      ...
    - NTU_RGBD_samples_with_missing_skeletons.txt
    - NTU_RGBD120_samples_with_missing_skeletons.txt

Generating Data

1. NTU RGB+D 120
   • cd data_gen
   • python3 ntu120_gendata.py

Training & Testing

To train a new GCN-LogsigRNN model run:

python3 main.py --config ./config/ntu_sub/train_joint.yaml --device 0

• The model used is in model/gcn_logsigRNN.py