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SSCLS

SSCLS is the official implementataion of our Small-Scale human action CLaSsification model pipeline. We build this project base on Open Source Projection MMAction2 and PYSKL


SSCLS model pipeline

Supported Skeleton Datasets

Installation

git clone https://github.com/eunseo-v/sscls
conda create -n sscls python=3.8
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
conda activate sscls
pip install openmim
mim install mmcv-full==1.5.0
mim install mmdet
mim install mmpose
cd sscls
pip install -r requirements.txt
pip install -e .

Data Preparation

We provide segmented Nursing Activity and Tai Chi action datasets and the heatmap conversion program to transform the 3D skeleton coordinates to 2D heatmaps. To obtain the human skeleton annotations, you can:

Nursing Activities

  1. Download raw csv files from website https://ieee-dataport.org/competitions/nurse-care-activity-recognition-challenge.
  2. Generate npy files by python project_utils/ncrc_npy.py .
  3. Generate heatmap files by python project_utils/gen_ncrc_set.py.

Tai Chi Dataset

  1. Download segmented dataset from Google Drive
  2. Generate heatmap files by bash order/gen_tc.sh

You can use vis_skeleton to visualize the provided skeleton data.

Training & Testing

Training

We have conducted a series of experiments including:

  • Pre-training models bash order/ntu120.sh
  • Lr grid search in LP bash order/exp0.sh
  • Impact of data preprocessing in LP bash order/exp1.sh
  • Effectiveness of joint segment strategy bash order/exp2.sh
  • Effectiveness of pre-training strategy bash order/exp3.sh
  • Multimodality and Late fusion bash order/exp4.sh
  • Lr grid search and pre-training comparison in FT bash order/exp5.sh
  • Stable Results with different data preprocessing methods bash order/exp6.sh
  • Results on Nursign Activities bash order/exp7.sh
  • Results on Tai Chi bash order/exp8.sh

Testing

bash order/test.sh

Contact

For any questions, feel free to contact: _eunseo_v@hit.edu.cn

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