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
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 .
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:
- Download raw csv files from website https://ieee-dataport.org/competitions/nurse-care-activity-recognition-challenge.
- Generate npy files by
python project_utils/ncrc_npy.py
. - Generate heatmap files by
python project_utils/gen_ncrc_set.py
.
- Download segmented dataset from Google Drive
- Generate heatmap files by
bash order/gen_tc.sh
You can use vis_skeleton to visualize the provided skeleton data.
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
bash order/test.sh
For any questions, feel free to contact: _eunseo_v@hit.edu.cn