This is the pytorch implementation of our paper DDAD: Detachable Crowd Density Estimation Assisted Pedestrian Detection, published in IEEE Transactions on Intelligent Transportation Systems (T-ITS) 2023.
Install dependencies:
pip install -r requirements.txt
Training.
More training seetings can be set in config.py
cd tools
python train.py -md rcnn_fpn_baseline
python train.py -md rcnn_emd_simple_idad
Testing.
cd tools
python test.py -md rcnn_fpn_baseline -r 1
python test.py -md rcnn_emd_simple_idad -r 1
When testing different models, simply modify the numbers in the test commands. The result json file will be evaluated automatically.
Models are avaliable in the model zoo, code: 4i2x.
Mat files are in the mat files, code: fpga.
This is the repository for the paper MarginMatch: DDAD: Detachable Crowd Density Estimation Assisted Pedestrian Detection. If you found this repository helpful, consider citing our paper:
@article{tang2022ddad,
title={DDAD: Detachable Crowd Density Estimation Assisted Pedestrian Detection},
author={Tang, Wenxiao and Liu, Kun and Shakeel, M Saad and Wang, Hao and Kang, Wenxiong},
journal={IEEE Transactions on Intelligent Transportation Systems},
volume={24},
number={2},
pages={1867--1878},
year={2022},
publisher={IEEE}
}