---
license: mit
task_categories:
  - time-series-forecasting
  - robotics
  - video-classification
  - feature-extraction
tags:
  - blender
  - camera-tracking
  - vfx
  - optical-flow
  - computer-vision
pretty_name: AutoSolve Telemetry
size_categories:
  - n<1K
---

# 🧪 AutoSolve Research Dataset (Beta)

> **Community-driven telemetry for 3D Camera Tracking**

This dataset collects anonymized tracking sessions from the [AutoSolve Blender Addon](https://github.com/UsamaSQ/AutoSolve). It trains an adaptive learning system that predicts optimal tracking settings (Search Size, Pattern Size, Motion Models) based on footage characteristics.

---

## 🤝 How to Contribute

Your data makes AutoSolve smarter for everyone.

### Step 1: Export from Blender

1. Open Blender and go to the **Movie Clip Editor**.
2. In the **AutoSolve** panel, find the **Research Beta** sub-panel.
3. Click **Export** (exports as `autosolve_telemetry_YYYYMMDD_HHMMSS.zip`).

### Step 2: Upload Here

1. Click the **"Files and versions"** tab at the top of this page.
2. Click **"Add file"** → **"Upload file"**. (You need to be Logged-In to HuggingFace to upload)
3. Drag and drop your `.zip` file.
4. _(Optional)_ Add a brief description: e.g., "10 drone shots, 4K 30fps, outdoor"
5. Click **"Commit changes"** (creates a Pull Request).

**Note:** Contributions are reviewed before merging to ensure data quality and privacy compliance.

### Step 3: Join the Community

Have questions or want to discuss your contributions?

**Discord:** [Join our community](https://discord.gg/qUvrXHP9PU)  
**Documentation:** [Full contribution guide](https://github.com/UsamaSQ/AutoSolve/blob/main/CONTRIBUTING_DATA.md)

---

## 📊 Dataset Structure

Each ZIP file contains anonymized numerical telemetry:

### 1. Session Records (`/sessions/*.json`)

Individual tracking attempts with complete metrics.

**What's Included:**

- **Footage Metadata:** Resolution, FPS, Frame Count
- **Settings Used:** Pattern Size, Search Size, Correlation, Motion Model
- **Results:** Solve Error, Bundle Count, Success/Failure
- **Camera Intrinsics:** Focal Length, Sensor Size, Distortion Coefficients (K1, K2, K3)
- **Motion Analysis:** Motion Class (LOW/MEDIUM/HIGH), Parallax Score, Velocity Statistics
- **Feature Density:** Count of trackable features per 9-grid region (from Blender's detect_features)
- **Time Series:** Per-frame active tracks, dropout rates, velocity profiles
- **Track Lifecycle:** Per-marker survival, jitter, reprojection error
- **Track Healing:** Anchor tracks, healing attempts, gap interpolation results
- **Track Averaging:** Merged segment counts

**Example Session:**

```json
{
  "schema_version": 1,
  "timestamp": "2025-12-12T10:30:00",
  "resolution": [1920, 1080],
  "fps": 30,
  "frame_count": 240,
  "settings": {
    "pattern_size": 17,
    "search_size": 91,
    "correlation": 0.68,
    "motion_model": "LocRot"
  },
  "success": true,
  "solve_error": 0.42,
  "bundle_count": 45,
  "motion_class": "MEDIUM",
  "visual_features": {
    "feature_density": {
      "center": 12,
      "top-left": 8,
      "top-right": 6
    },
    "motion_magnitude": 0.015,
    "edge_density": {
      "center": 0.85,
      "top-left": 0.42
    }
  }
  "healing_stats": {
    "candidates_found": 5,
    "heals_attempted": 3,
    "heals_successful": 2,
    "avg_gap_frames": 15.0
  }
}
```

### 2. Behavior Records (`/behavior/*.json`)

**THE KEY LEARNING DATA** - How experts improve tracking.

**What's Captured:**

- **Track Additions:** 🔑 Which markers users manually add (region, position, quality)
- **Track Deletions:** Which markers users remove (region, lifespan, error, reason)
- **Settings Adjustments:** Which parameters users changed (before/after values)
- **Re-solve Results:** Whether user changes improved solve error
- **Marker Refinements:** Manual position adjustments
- **Net Track Change:** How many tracks were added vs removed
- **Region Reinforcement:** Which regions pros manually populated

**Purpose:** Teaches the AI how experts **improve** tracking, not just cleanup.

**Example Behavior:**

```json
{
  "schema_version": 1,
  "clip_fingerprint": "a7f3c89b2e71d6f0",
  "contributor_id": "x7f2k9a1",
  "iteration": 3,
  "track_additions": [
    {
      "track_name": "Track.042",
      "region": "center",
      "initial_frame": 45,
      "position": [0.52, 0.48],
      "lifespan_achieved": 145,
      "had_bundle": true,
      "reprojection_error": 0.32
    }
  ],
  "track_deletions": [
    {
      "track_name": "Track.003",
      "region": "top-right",
      "lifespan": 12,
      "had_bundle": false,
      "reprojection_error": 2.8,
      "inferred_reason": "high_error"
    }
  ],
  "net_track_change": 3,
  "region_additions": { "center": 2, "bottom-center": 1 },
  "re_solve": {
    "attempted": true,
    "error_before": 0.87,
    "error_after": 0.42,
    "improvement": 0.45,
    "improved": true
  }
}
```

### 3. Model State (`model.json`)

The user's local statistical model state showing learned patterns.

---

## 📋 What Gets Collected

Each contribution includes:

✅ **Numerical Metrics**

- Tracking settings that worked (or failed)
- Motion analysis (velocity, direction, parallax)
- Per-track survival and quality metrics
- Feature density counts per region

✅ **Camera Characteristics**

- Focal length and sensor size
- Lens distortion coefficients
- Principal point coordinates

✅ **Time Series Data**

- Per-frame active track counts
- Track dropout rates
- Velocity profiles over time

---

## 🔒 Data Privacy & Ethics

We take privacy seriously. This dataset contains **numerical telemetry only**.

❌ **NOT Collected:**

- Images, video frames, or pixel data
- File paths or project names
- User identifiers (IPs, usernames, emails)
- System information

✅ **Only Collected:**

- Resolution, FPS, frame count
- Mathematical motion vectors
- Tracking settings and success metrics
- Feature density counts (not actual features)

_For complete schema documentation, see [TRAINING_DATA.md](https://github.com/UsamaSQ/AutoSolve/blob/main/TRAINING_DATA.md)_

---

## 🛠 Usage for Researchers

This data is ideal for training models related to:

### Hyperparameter Optimization

Predicts optimal tracking settings (Search Size, Pattern Size, Correlation, Motion Models) based on footage characteristics and motion analysis.

### Outlier Detection

Identifying "bad" 2D tracks before camera solve using lifecycle and jitter patterns.

### Motion Classification

Classifying camera motion types (Drone, Handheld, Tripod) from sparse optical flow and feature density.

### Temporal Modeling

Predicting track dropout using RNN/LSTM trained on per-frame time series data.

---

## 💻 Loading the Dataset

### Python Example

```python
import json
import zipfile
from pathlib import Path
from collections import defaultdict

# Load a contributed ZIP
zip_path = Path('autosolve_telemetry_20251212_103045.zip')

with zipfile.ZipFile(zip_path, 'r') as zf:
    # Read manifest
    manifest = json.loads(zf.read('manifest.json'))
    print(f"Export Version: {manifest['export_version']}")
    print(f"Sessions: {manifest['session_count']}")
    print(f"Behaviors: {manifest['behavior_count']}")

    # Load all sessions
    sessions = []
    for filename in zf.namelist():
        if filename.startswith('sessions/') and filename.endswith('.json'):
            session_data = json.loads(zf.read(filename))
            sessions.append(session_data)

    # Analyze by footage class
    by_class = defaultdict(list)
    for s in sessions:
        width = s['resolution'][0]
        fps = s['fps']
        motion = s.get('motion_class', 'MEDIUM')
        cls = f"{'HD' if width >= 1920 else 'SD'}_{int(fps)}fps_{motion}"
        by_class[cls].append(s['success'])

    # Success rates per class
    print("\nSuccess Rates by Footage Class:")
    for cls, results in sorted(by_class.items()):
        rate = sum(results) / len(results)
        print(f"  {cls}: {rate:.1%} ({len(results)} sessions)")
```

### Feature Extraction Example

```python
# Extract feature density patterns
feature_densities = []
for session in sessions:
    vf = session.get('visual_features', {})
    density = vf.get('feature_density', {})
    if density:
        feature_densities.append({
            'motion_class': session.get('motion_class'),
            'center': density.get('center', 0),
            'edges': sum([
                density.get('top-left', 0),
                density.get('top-right', 0),
                density.get('bottom-left', 0),
                density.get('bottom-right', 0)
            ]) / 4,
            'success': session['success']
        })

# Analyze: Do edge-heavy clips succeed more?
import pandas as pd
df = pd.DataFrame(feature_densities)
print(df.groupby('success')['edges'].mean())
```

---

## 📈 Dataset Statistics

**Current Status:** Beta Collection Phase

**Target:**

- 100+ unique footage types
- 500+ successful tracking sessions
- Diverse motion classes and resolutions

**Contribute** to help us reach production-ready dataset size! 🚀

---

## 📖 Citation

If you use this dataset in your research, please cite:

```bibtex
@misc{autosolve-telemetry-2025,
  title={AutoSolve Telemetry: Community-Driven Camera Tracking Dataset},
  author={Bin Shahid, Usama},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/UsamaSQ/autosolve-telemetry}
}
```

---

## 🤝 Community & Support

**Repository:** [GitHub.com/UsamaSQ/AutoSolve](https://github.com/UsamaSQ/AutoSolve)  
**Discord:** [Join our community](https://discord.gg/qUvrXHP9PU)  
**Maintainer:** Usama Bin Shahid

Your contributions make AutoSolve better for everyone! 🙏