A Light CNN based Method for Hand Detection and Orientation Estimation
This project utilizes a modified MobileNet in company with the SSD framework to achieve a robust and fast detection of hand location and orientation.
Our implementation is based on the PyTorch version of SSD and MobileNet.
Due to some compatibility issues, we recommend to install PyTorch 0.3.0 and Python 3.6.8, which our project currently supports.
Get the code.
git clone https://github.com/yangli18/hand_detection.git
sh data/scripts/Oxford_hand_dataset.sh
sh layers/src/make.sh
Train the detection model on the Oxford hand dataset.
python train.py 2>&1 | tee log/train.log
weights
dir.weights
dir.Evaluate the trained detection model.
python eval.py --trained_model weights/ssd_new_mobilenet_FFA.pth --version ssd_new_mobilenet_FFA
Evaluate the average detection time.
python eval_speed.py --trained_model weights/ssd_new_mobilenet_FFA.pth --version ssd_new_mobilenet_FFA
If you find our code useful, please cite our paper.
@inproceedings{yang2018light,
title={A Light CNN based Method for Hand Detection and Orientation Estimation},
author={Yang, Li and Qi, Zhi and Liu, Zeheng and Zhou, Shanshan and Zhang, Yang and Liu, Hao and Wu, Jianhui and Shi, Longxing},
booktitle={2018 24th International Conference on Pattern Recognition (ICPR)},
pages={2050--2055},
year={2018},
organization={IEEE}
}
@article{yang2019embedded,
title={An embedded implementation of CNN-based hand detection and orientation estimation algorithm},
author={Yang, Li and Qi, Zhi and Liu, Zeheng and Liu, Hao and Ling, Ming and Shi, Longxing and Liu, Xinning},
journal={Machine Vision and Applications},
volume={30},
number={6},
pages={1071--1082},
year={2019},
publisher={Springer}
}