---
license: apache-2.0
task_categories:
- feature-extraction
language:
- ja
size_categories:
- n<1K
---

# Japanese TV Commercial Video Dataset

## Dataset Description

This dataset contains Japanese TV commercial (CM) videos with hierarchical scene detection annotations.

### Dataset Summary

- **Total Videos**: 100
- **Total Duration**: 39.5 minutes (2370 seconds)
- **Total Scenes**: 2,320
- **Average Duration per Video**: 23.7s
- **Average Scenes per Video**: 23.2

### Languages

Commercial videos contain a mix of:
- Japanese (primary)
- English (secondary)

## Dataset Structure

```
dataset/
├── videos/              # Video files (.mp4)
│   ├── CM_000.mp4
│   ├── CM_001.mp4
│   └── ...
├── thumbnails/          # Representative thumbnails for each video
│   ├── CM_000.jpg
│   ├── CM_001.jpg
│   └── ...
├── scenes/              # Scene detection results
│   ├── CM_000/
│   │   ├── scenes.json  # Scene boundaries and metadata
│   │   └── scene_*.jpg  # Thumbnails for each scene
│   └── ...
├── metadata.parquet     # Dataset metadata (recommended)
└── metadata.csv         # Human-readable metadata

```

### Data Fields

**metadata.parquet / metadata.csv**:
- `video_id`: Unique identifier (e.g., "CM_000")
- `video_path`: Path to video file
- `thumbnail`: Path to representative thumbnail image
- `duration`: Video duration in seconds
- `num_scenes`: Total number of detected scenes
- `num_groups`: Number of scene groups
- `scenes_json_path`: Path to scenes.json file
- `scene_thumbnails_dir`: Directory containing scene thumbnails
- `level1_count`, `level2_count`, `level3_count`: Scene counts per detection level
- `level1_threshold`, `level2_threshold`, `level3_threshold`: Detection thresholds used

**scenes.json** (per video):
```json
{
  "video_name": "CM_000",
  "video_duration": 29.66,
  "total_scenes": 15,
  "scenes": [
    {
      "scene_number": 1,
      "start_time": 0.0,
      "end_time": 2.102,
      "duration": 2.102,
      "start_timecode": "00:00:00.000",
      "end_timecode": "00:00:02.102",
      "thumbnail": "scene_001.jpg",
      "level": 1,
      "threshold": 5.0
    }
  ]
}
```

## Scene Detection Methodology

Scenes are detected using hierarchical scene detection with PySceneDetect:
- **Level 1** (threshold: 5.0): Major scene changes (coarse)
- **Level 2** (threshold: 3.0): Medium scene changes
- **Level 3** (threshold: 1.0): Subtle scene changes (fine)

Each scene includes:
- Precise start/end timestamps
- Duration
- Detection level (indicating cut intensity)
- Thumbnail image

## Usage

### Load with Hugging Face Datasets

```python
from datasets import load_dataset

# Load metadata
dataset = load_dataset("your-username/tv-commercial-videos")

# Access first video
sample = dataset["train"][0]
print(f"Video: {sample['video_path']}")
print(f"Duration: {sample['duration']}s")
print(f"Scenes: {sample['num_scenes']}")
```

### Load with Pandas

```python
import pandas as pd

# Load metadata
df = pd.read_parquet("metadata.parquet")

# Load scene data for specific video
import json
with open(df.iloc[0]['scenes_json_path'], 'r') as f:
    scenes = json.load(f)
```

## Use Cases

This dataset is suitable for:
- **Video segmentation**: Scene boundary detection
- **Content analysis**: Commercial structure analysis
- **Computer vision**: Object detection in commercial contexts
- **Temporal analysis**: Shot duration patterns
- **Multi-modal learning**: Video + audio + text
- **Advertisement research**: Creative patterns in commercials

## Limitations

- Videos are sourced from Japanese TV commercials (specific domain)
- Scene detection is automated and may have occasional errors
- No manual verification of scene boundaries
- No semantic labels (e.g., product categories, themes)

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{tv_commercial_videos_2024,
  title={Japanese TV Commercial Video Dataset with Scene Detection},
  author={Your Name},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/your-username/tv-commercial-videos}
}
```

## License

This dataset is released under CC-BY-4.0 license.

## Contact

For questions or issues, please open an issue on the dataset repository.