Update README.md

README.md

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 ---
 license: cc-by-4.0
+task_categories:
+- object-detection
+tags:
+- rgb-d
+- video-object-counting
+- crowded-scenes
+- occlusion
 ---
+
+# 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
+
+```text
+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.
+
+## Associated Publication
+
+This dataset is introduced in the following paper:
+
+**Depth-Guided Video Object Counting in Crowded Scenes**
+
+Accepted at the 34th ACM International Conference on Multimedia (ACM Multimedia 2026, ACM MM 2026).
+
+The final author list, DOI, proceedings information, and paper link will be added after the official publication.
+
+
+## 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](https://creativecommons.org/licenses/by/4.0/). Users must provide appropriate attribution when using, modifying, or redistributing this dataset.
+
+## Related Resources
+
+- Model weights: https://huggingface.co/aerospace123/DG-Net
+- Dataset: https://huggingface.co/datasets/aerospace123/RGBD-VideoCount
+- Code: https://github.com/streamer-AP/DG-Net