VisDrone_CC

This is the Soft CSRNET version repo for ECCV2020(Challenge-CrowdCounting), which delivered an optimization of the parameters of the deep CNN "CSRNET" which made the density estimation in real time.

Datasets

CC visdrone Dataset: web_site

Prerequisites

We used Google Colab as a perncipal environment to train and test our models.

Ground Truth

Please follow the DensityVisDrone.ipynb to generate the ground truth. You may use Visdrone2019_dotAnnotation.ipynb to change the type of the annotation in Visdrone VID 2019 dataset or else.

Training Process

Please follow the first part of TrainVal.ipynb to start training process.

Validation

Follow the second part TrainVal.ipynb to try the validation. You can try to modify the notebook and see the output of each image.

Test

Follow the Test_model.ipynb to test your model on 2020 ECCV CC. You can try to modify the notebook and see the output of each image.

References

Code

On this repos we based on the keras implementation of CSRNet by : https://github.com/Neerajj9/CSRNet-keras
We're finalyzing also the Pytorch implementation : https://github.com/imenebak/CSRNet-pytorch

SOFT-CSRNet paper

@INPROCEEDINGS{9378749,
  author={Bakour, Imene and Bouchali, Hadia Nesma and Allali, Sarah and Lacheheb, Hadjer},
  booktitle={2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH)}, 
  title={Soft-CSRNet: Real-time Dilated Convolutional Neural Networks for Crowd Counting with Drones}, 
  year={2021},
  volume={},
  number={},
  pages={28-33},
  doi={10.1109/IHSH51661.2021.9378749}}
  

CSRNet paper.

@inproceedings{li2018csrnet,
  title={CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes},
  author={Li, Yuhong and Zhang, Xiaofan and Chen, Deming},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={1091--1100},
  year={2018}
}

ECCV2020 Challenge DroneCrowd.

@article{zhu2018vision,

title={Vision meets drones: A challenge},
author={Zhu, Pengfei and Wen, Longyin and Bian, Xiao and Ling, Haibin and Hu, Qinghua},
journal={arXiv preprint arXiv:1804.07437},
year={2018} }

@article{zhu2020vision,
title={Vision Meets Drones: Past, Present and Future},
author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Hu, Qinghua and Ling, Haibin},
journal={arXiv preprint arXiv:2001.06303},
year={2020} }