cm_dataset_small / README.md
dev-trustedai's picture
Upload folder using huggingface_hub
50fd1e7 verified
|
raw
history blame
4.38 kB
---
license: cc-by-4.0
task_categories:
- video-classification
- object-detection
- video-segmentation
language:
- ja
- en
size_categories:
- 1K<n<10K
---
# 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.