---
license: mit
task_categories:
- video-classification
- zero-shot-classification
tags:
- motion-detection
- temporal-vision
- speckle-noise
size_categories:
- n<1K
---

# Motion-Text Classifier Dataset

A binary classification dataset for distinguishing between pure motion and text-revealed-through-motion in speckle noise videos.

## Dataset Description

This dataset contains 1000 videos (5 seconds each) designed to test motion-based perception:

- **Motion-only (500 videos)**: Pure background noise scrolling horizontally
- **Text videos (500 videos)**: Single words revealed through opposing foreground/background motion

All videos use identical noise parameters to ensure the **only** distinguishing feature is the presence of foreground content.

## Dataset Structure

```
dataset/
├── train/
│   ├── motion_0000.mp4 to motion_0449.mp4 (450 videos)
│   ├── text_0000.mp4 to text_0449.mp4 (450 videos)
│   └── metadata.csv
└── test/
    ├── motion_0450.mp4 to motion_0499.mp4 (50 videos)
    ├── text_0450.mp4 to text_0499.mp4 (50 videos)
    └── metadata.csv
```

## Usage

```python
from datasets import load_dataset

# Load dataset
dataset = load_dataset("mukul54/motion-text-classifier")

# Access samples
train_sample = dataset['train'][0]
print(train_sample['label'])  # 'motion_only' or 'text_fg_bg'
print(train_sample['text'])   # word shown (None for motion_only)
```

## Video Parameters

All videos share identical generation parameters:

| Parameter | Value |
|-----------|-------|
| Resolution | 960×540 |
| FPS | 60 |
| Duration | 5.0 seconds |
| Noise Density | 0.5 |
| Speckle Size | 1 |
| Speed | 2 |
| Direction | Horizontal |

## Labels

- **motion_only**: Background noise only (no foreground object)
- **text_fg_bg**: Text revealed through opposite motion of foreground/background using the same noise pattern

## Key Features

- **Pure motion detection**: Text is invisible in static frames
- **Controlled experiment**: All parameters identical except foreground content
- **Temporal encoding**: Information only available through motion analysis

## Use Cases

- Testing motion perception in vision models
- Temporal feature extraction benchmarks
- Zero-shot video understanding
- Motion-based object detection

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{motion_text_classifier,
  title={Motion-Text Classifier: Speckle Noise Motion Detection Dataset},
  author={Mukul},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/mukul54/motion-text-classifier}
}
```

## License

MIT License