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MovingDroneCrowd++

MovingDroneCrowd++ is a large-scale video-level dataset dedicated to dense crowd counting and tracking from fast-moving drones. It was introduced in the paper Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods (Extended version) and Video Individual Counting for Moving Drones (ICCV 2025).

Dataset Description

MovingDroneCrowd++ captures dense crowds under diverse flight altitudes, camera angles, and illumination conditions. Each pedestrian head is annotated with a bounding box and an identity ID across frames, enabling tasks such as:

  • Video individual counting (VIC)
  • Inflow/outflow analysis
  • Dense crowd tracking

Dataset Structure

The expected directory structure for the dataset is as follows:

MovingDroneCrowd++/
+-- frames/
|   +-- scene_1/
|       +-- 1/
|           +-- 1.jpg
|           +-- 2.jpg
|           +-- ...
+-- annotations/
|   +-- scene_1/
|       +-- 1.csv
+-- val.txt
+-- train.txt
+-- test.txt
+-- scene_labels.txt
+-- MDC_val.txt
+-- MDC_train.txt
+-- MDC_test.txt

Annotation Format

Each annotation row follows the MOT-style format:

frame_id, person_id, x, y, w, h, -1, -1, -1, -1

The first column is the frame index, the second is the pedestrian identity, and the third to sixth columns are the head bounding box (x, y, w, h).

Sample Usage

You can download the dataset using the Hugging Face CLI:

huggingface-cli download fyw1999/MovingDroneCrowd --repo-type dataset --local-dir /path/to/MovingDroneCrowd++

Citation

@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 bit 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}
}
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