README.md

---
license: cc-by-4.0
task_categories:
- video-classification
- question-answering
language:
- en
tags:
- video-understanding
- temporal-reasoning
- counting
- benchmark
- streaming
size_categories:
- 1K<n<10K
pretty_name: SVCBench
---

# SVCBench: Streaming Video Counting Benchmark

This dataset contains the **clipped video segments** for **SVCBench**, a Streaming Video Counting Benchmark for Spatial-Temporal State Maintenance. It repositions counting as a minimal, controlled probe for diagnosing how video understanding models maintain world state along the video timeline.

- **Project Page**: https://buaa-colalab.github.io/SVCBench/
- **Code**: https://github.com/buaa-colalab/SVCBench

## Dataset Description

This repository provides **4,574 clipped video segments** used by SVCBench, designed for evaluating spatial-temporal state maintenance capabilities in video understanding models.

### Dataset Summary

- **Total Videos**: 4,574 clips
- **Total Size**: ~80 GB
- **Video Format**: MP4 (H.264)
- **Categories**: 8 subcategories across object counting and event counting tasks

### Categories

**Object Counting (2,297 clips)**:
- `O1-Snap`: Current-state snapshot (252 clips)
- `O1-Delta`: Current-state delta (98 clips)
- `O2-Unique`: Global unique counting (1,869 clips)
- `O2-Gain`: Windowed gain counting (78 clips)

**Event Counting (2,277 clips)**:
- `E1-Action`: Instantaneous action (1,281 clips)
- `E1-Transit`: State transition (205 clips)
- `E2-Periodic`: Periodic action (280 clips)
- `E2-Episode`: Episodic segment (511 clips)

## File Naming Convention

### Multi-query clips
Format: `{category}_{question_id}_{query_index}.mp4`

Example: `e1action_0000_00.mp4`, `e1action_0000_01.mp4`

### Single-query clips
Format: `{category}_{question_id}.mp4`

Example: `o1delta_0007.mp4`, `o2gain_0000.mp4`

## Video Properties

- **Encoding**: H.264 (using `-c copy` for lossless clipping)
- **Frame Rates**: Preserved from source (3fps, 24fps, 25fps, 30fps, 60fps)
- **Duration Accuracy**: ±0.1s from annotation timestamps
- **Quality**: Original quality maintained (no re-encoding)

## Source Datasets

Videos are clipped from multiple source datasets:
- YouTube walking tours and sports videos
- RoomTour3D (indoor navigation)
- Ego4D (first-person view)
- ScanNet, ScanNet++, ARKitScenes (3D indoor scenes)
- TOMATO, CODa, OmniWorld (temporal reasoning)
- Simulated physics videos

## Usage

### Loading with Python

```python
from huggingface_hub import hf_hub_download
import cv2

# Download a specific video
video_path = hf_hub_download(
    repo_id="buaaplay/SVCBench",
    filename="e1action_0000_00.mp4",
    repo_type="dataset"
)

# Load with OpenCV
cap = cv2.VideoCapture(video_path)
```

### Batch Download

```bash
# Install huggingface-cli
pip install huggingface_hub

# Download entire dataset
huggingface-cli download buaaplay/SVCBench --repo-type dataset --local-dir ./svcbench_videos
```

## Annotations

For complete annotations including questions, query points, and ground truth answers, please refer to the SVCBench code repository (https://github.com/buaa-colalab/SVCBench):
- Object counting annotations: `object_count_data/*.json`
- Event counting annotations: `event_counting_data/*.json`

Each annotation file contains:
- `id`: Question identifier
- `source_dataset`: Original video source
- `video_path`: Original video filename
- `question`: Counting question
- `query_time` or `query_points`: Timestamp(s) for queries
- `count`: Ground truth answer(s)

## Quality Validation

All videos have been validated for:
- ✓ Duration accuracy (100% within ±0.1s)
- ✓ Frame rate preservation (original fps maintained)
- ✓ No frame drops or speed changes
- ✓ Lossless clipping (no re-encoding artifacts)

## Citation

If you use this dataset, please cite the SVCBench paper:

```bibtex
@inproceedings{liu2026svcbench,
  title     = {SVCBench: A Streaming Video Counting Benchmark for
               Spatial-Temporal State Maintenance},
  author    = {Liu, Pengyiang and Shi, Zhongyue and Hao, Hongye and Fu, Qi and
               Bi, Xueting and Zhang, Siwei and Hu, Xiaoyang and Wang, Zitian and
               Huang, Linjiang and Liu, Si},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}
```

## Licensing

- Annotations (parquet/JSON) and evaluation code: CC-BY-4.0
- Self-generated physics-simulation videos: CC-BY-4.0
- Source videos: retain their respective licenses — Ego4D (Ego4D License), ScanNet / ScanNet++ (respective Terms of Use, non-commercial research), ARKitScenes (Apple ARKitScenes license), RoomTour3D (CC-BY-NC), CODa (Apache-2.0), OmniWorld (CC-BY-NC), TOMATO (academic / non-commercial only; self-created clips CC BY-NC-SA, YouTube-sourced clips under original Creative Commons licenses), and other YouTube content (platform / creator terms). The E2-Periodic clips are loop-concatenation derivatives of TOMATO and are therefore released for non-commercial research under CC BY-NC-SA, with attribution to TOMATO and the original video creators.

By downloading this dataset, you agree to adhere to CC-BY-4.0 and to the licenses of all source datasets. If you are a rights holder with any concern about specific content, please open an issue and we will address it promptly.

## Dataset Statistics

| Category | Clips | Avg Duration | Total Size |
|----------|-------|--------------|------------|
| O1-Snap | 252 | ~2min | ~4.3 GB |
| O1-Delta | 98 | ~1min | ~1.7 GB |
| O2-Unique | 1,869 | ~3min | ~32 GB |
| O2-Gain | 78 | ~1min | ~1.3 GB |
| E1-Action | 1,281 | ~4min | ~28 GB |
| E1-Transit | 205 | ~2min | ~3.5 GB |
| E2-Periodic | 280 | ~3min | ~8.7 GB |
| E2-Episode | 511 | ~2min | ~4.8 GB |
| **Total** | **4,574** | - | **~80 GB** |

## Contact

For questions or issues, please open an issue in the [SVCBench repository](https://github.com/buaa-colalab/SVCBench).