This repository provides pretrained weights for MovingDroneCrowd and MovingDroneCrowd++, as presented in the following works:
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This repository provides pretrained weights for MovingDroneCrowd and MovingDroneCrowd++, as presented in the following works:
Fontes
1 fonteVerificado 9 de set.
Artefatos de modelo
9 artefatosTrechos de fonte
2 trechosThe repository includes models for video individual counting and tracking from moving drones under diverse aerial conditions.
You can download all pretrained weights using the huggingface_hub library:
from huggingface_hub import snapshot_download
snapshot_download(repo_id="fyw1999/MovingDroneCrowd-Weights")
For detailed instructions on training and evaluation, please refer to the official GitHub repository.
If this project helps your research, please cite the following papers:
@article{MDC++_GD3A,
title={Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods},
author={Fan, Yaowu and Wan, Jia and Han, Tao and Ma, Andy J. and Ouyang, Wanli and Chan, Antoni B.},
journal={arXiv preprint arXiv:2601.12500},
year={2026}
}
@inproceedings{MDC_SDNet,
title={Video Individual Counting for Moving Drones},
author={Fan, Yaowu and Wan, Jia Friend and Han, Tao and Chan, Antoni B. and Ma, Andy J.},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month={October},
year={2025},
pages={12284--12293}
}
GD3A_pre_trained_global_counter_STEERER_MDC++_ep_201_mae_13.5_mse_19.1.pth
pth · 248 MB · SHA-256 40d5e20447c0…7b00 · Hugging Face
GD3A_pre_trained_global_counter_VGG16_FPN_VSCrowd_kernel_size_25_epoch_50.pth
pth · 79,8 MB · SHA-256 403305adb9c3…4882 · Hugging Face
BaixarSDNet_pre_trained_counter_MDC_VGG16_FPN_ep_200_downscale_16.pth
pth · 82,0 MB · SHA-256 5b9f7271f441…8022 · Hugging Face
BaixarSDNet_pre_trained_counter_MDC++_VGG16_FPN_ep_150_downscale_16.pth
pth · 82,0 MB · SHA-256 87fb5df9283b…8fb2 · Hugging Face
BaixarSDNet_pre_trained_counter_VSCrowd_VGG16_FPN_ep_100_downscale_16.pth
pth · 82,0 MB · SHA-256 f75b4a7dc90c…b3a1 · Hugging Face
Baixar--- datasets: - fyw1999/MovingDroneCrowd language: - en license: cc-by-nc-4.0 pipeline_tag: object-detection library_name: pytorch tags: - moving-drone - video-individual-counting - dense-crowd-tracking --- # MovingDroneCrowd-Weights This repository provides pretrained weights for **MovingDroneCrowd** and **MovingDroneCrowd++**, as presented in the following works: - **Extended version**: [Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods](https://huggingface.co/papers/2601.12500) - **Conference version**: [Video Individual Counting for Moving Drones](https://huggingface.co/papers/2503.10701) (ICCV 2025 Highlight) The repository includes models for video individual counting and tracking from moving drones under diverse aerial conditions. ## Links - **Code:** [https://github.com/fyw1999/MovingDroneCrowd](https://github.com/fyw1999/MovingDroneCrowd) - **Dataset:** [fyw1999/MovingDroneCrowd](https://huggingface.co/datasets/fyw1999/MovingDroneCrowd) ## Methods - **SDNet**: A video individual counting method that predicts shared density maps between adjacent frames via cross-frame attention. - **GD³A**: Global Density map Decomposition via group-wise Descriptor Association, providing a more interpretable and efficient video individual counting method. - **DVTrack**: A pedestrian tracking method (Descriptor Voting Track) that converts descriptor-level matching into instance-level associations. ## Usage You can download all pretrained weights using the `huggingface_hub` library: ```python from huggingface_hub import snapshot_download snapshot_download(repo_id="fyw1999/MovingDroneCrowd-Weights") ``` For detailed instructions on training and evaluation, please refer to the [official GitHub repository](https://github.com/fyw1999/MovingDroneCrowd). ## Citation If this project helps your research, please cite the following papers: ```bibtex @article{MDC++_GD3A, title={Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods}, author={Fan, Yaowu and Wan, Jia and Han, Tao and Ma, Andy J. and Ouyang, Wanli and Chan, Antoni B.}, journal={arXiv preprint arXiv:2601.12500}, year={2026} } @inproceedings{MDC_SDNet, title={Video Individual Counting for Moving Drones}, author={Fan, Yaowu and Wan, Jia Friend and Han, Tao and Chan, Antoni B. and Ma, Andy J.}, booktitle={Proceedings of the IEEE/CVF...
Source context: 0 downloads · 0 likes · Pipeline object-detection · Library pytorch · Repo fyw1999/MovingDroneCrowd-Weights