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Datasets:
Quanad313
/
MovingDroneCrowd

Tasks:
Object Detection
Languages:
English
ArXiv:
Tags:
crowd-counting
tracking
drone-imagery
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label MovingDroneCrowd@fe7b79869b44b935c8d0ee94b315a559917e44e1
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label MovingDroneCrowd@fe7b79869b44b935c8d0ee94b315a559917e44e1

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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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