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Datasets:
aerospace123
/
RGBD-VideoCount

Tasks:
Object Detection
Modalities:
Image
Text
Tags:
RGB-D
video-object-counting
crowded-scenes
occlusion
License:
Dataset card Data Studio Files
xet
 Community
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The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code:   DatasetGenerationError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0x93 in position 0: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/text/text.py", line 98, in _generate_tables
                  batch = f.read(self.config.chunksize)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
                  out = read(*args, **kwargs)
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x93 in position 0: invalid start byte
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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text
string
{
"train": [
"RGB0001","RGB0003","RGB0005","RGB0006","RGB0007","RGB0010","RGB0011","RGB0012","RGB0013","RGB0015",
"RGB0016","RGB0018","RGB0019","RGB0020","RGB0023","RGB0024","RGB0025","RGB0026","RGB0027","RGB0028",
"RGB0029","RGB0030","RGB0032","RGB0033","RGB0034","RGB0035","RGB0036","RGB0037","RGB0040","RGB0041",
"RGB0042","RGB0044","RGB0045","RGB0046","RGB0048","RGB0049","RGB0050","RGB0051","RGB0052","RGB0053",
"RGB0054","RGB0055","RGB0058","RGB0061","RGB0062","RGB0063","RGB0069","RGB0070","RGB0071","RGB0072",
"RGB0075","RGB0076","RGB0077","RGB0078","RGB0079","RGB0080","RGB0081","RGB0082","RGB0083","RGB0084",
"RGB0086","RGB0087","RGB0089","RGB0090","RGB0091","RGB0093","RGB0095","RGB0196","RGB0097","RGB0098",
"RGB0099","RGB0101","RGB0102","RGB0104","RGB0105","RGB0108","RGB0109","RGB0110","RGB0111","RGB0112",
"RGB0118","RGB0119","RGB0120","RGB0122","RGB0123","RGB0124","RGB0125","RGB0126","RGB0130","RGB0131",
"RGB0132","RGB0133","RGB0134","RGB0135","RGB0136","RGB0137","RGB0138","RGB0139","RGB0141","RGB0142",
"RGB0143","RGB0144","RGB0145","RGB0148","RGB0149","RGB0152","RGB0153","RGB0156","RGB0157","RGB0158",
"RGB0159","RGB0160","RGB0161","RGB0162","RGB0163","RGB0165","RGB0166","RGB0167","RGB0168","RGB0170",
"RGB0171","RGB0173","RGB0174","RGB0176","RGB0179","RGB0180","RGB0181","RGB0182","RGB0185","RGB0186",
"RGB0189","RGB0192","RGB0193","RGB0194","RGB0195"
],
"val": [
"RGB0008","RGB0017","RGB0021","RGB0002","RGB0039","RGB0031","RGB0085","RGB0184","RGB0140","RGB0151",
"RGB0191","RGB0172","RGB0187","RGB0129","RGB0164","RGB0128","RGB0103","RGB0060","RGB0056","RGB0074",
"RGB0146","RGB0065","RGB0107","RGB0115","RGB0067","RGB0113","RGB0188","RGB0178","RGB0183","RGB0117"
],
"test": [
"RGB0004","RGB0009","RGB0014","RGB0022","RGB0038","RGB0043","RGB0088","RGB0154","RGB0047","RGB0169",
"RGB0150","RGB0155","RGB0092","RGB0094","RGB0100","RGB0127","RGB0059","RGB0057","RGB0073","RGB0147",
"RGB0064","RGB0106","RGB0116","RGB0066","RGB0114","RGB0190","RGB0177","RGB0175","RGB0121","RGB0068"
]
}
{
"exemplars": {
"RGB0004": {
"0": [
{
"image_path": "images/RGB0004/RGB0004_0000.png",
"bbox_xyxy": [
91.52,
349.59995999999995,
246.71999999999997,
687.20004
]
},
{
"image_path": "images/RGB0004/RGB0004_0000.png",
"bbox_xyxy": [
265.92,
355.99968,
401.92,
698.4
]
},
{
"image_path": "images/RGB0004/RGB0004_0000.png",
"bbox_xyxy": [
405.12,
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},
{
"image_path": "images/RGB0004/RGB0004_0001.png",
"bbox_xyxy": [
435.52,
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},
{
"image_path": "images/RGB0004/RGB0004_0001.png",
"bbox_xyxy": [
307.52000000000004,
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},
{
"image_path": "images/RGB0004/RGB0004_0001.png",
"bbox_xyxy": [
155.51999999999998,
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]
},
{
"image_path": "images/RGB0004/RGB0004_0002.png",
"bbox_xyxy": [
19.52,
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]
},
{
"image_path": "images/RGB0004/RGB0004_0002.png",
"bbox_xyxy": [
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End of preview.

RGBD-VideoCount

RGBD-VideoCount is an RGB-D video dataset for video object counting in crowded and occluded scenes. It provides synchronized RGB frames and depth maps, together with instance-level annotations for evaluating detection, cross-frame association, and video-level de-duplication.

Dataset Summary

  • 195 RGB-D video clips
  • 6 object categories
  • 2,032 finely annotated frames
  • 77,638 instance bounding boxes
  • Multi-category shelf and crowded-object scenes
  • RGB frames, aligned depth maps, instance annotations, counting annotations, data splits, and visual exemplars

Directory Structure 

RGBD-VideoCount/
|- images/
|- Depth_Data/
|- object_annotations/
|- count_annotations/
|- dataset_split.json
|- video_class.txt
|- exemplars_train.json
|- exemplars_val.json
`- exemplars_test.json

Data Description

  • images/: RGB video frames.
  • Depth_Data/: Depth maps aligned with RGB frames.
  • object_annotations/: Instance-level bounding-box annotations.
  • count_annotations/: Video-level counting annotations.
  • dataset_split.json: Training, validation, and test splits.
  • video_class.txt: Category metadata.
  • exemplars_*.json: Visual exemplars for exemplar-guided training and evaluation.

Citation

If you use this code, please cite our paper:

@inproceedings{xu2026depth,
  title     = {Depth-Guided Video Object Counting in Crowded Scenes},
  author    = {Xu, Yuanjing and Liu, Xinyan and Chen, Weidong and Zou, Zixuan and Zhang, Linhao and Meng, Zhuangzhe and Chan, Antoni B. and Zhang, Weigang},
  booktitle = {Proceedings of the 34th ACM International Conference on Multimedia},
  year      = {2026},
  doi       = {10.1145/3767308.3835482}
}

Limitations

RGBD-VideoCount focuses on crowded object scenes and may not represent all real-world environments. Performance can be affected by depth quality, severe appearance ambiguity, camera motion, and unseen object categories. Users are responsible for evaluating suitability before deployment in real applications.

License

RGBD-VideoCount is released under the Creative Commons Attribution 4.0 International License. Users must provide appropriate attribution when using, modifying, or redistributing this dataset.

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