> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/opentrack/opentrack/llms.txt
> Use this file to discover all available pages before exploring further.
# Easy Tracker
> Simplified point tracking with flexible model configurations
Easy Tracker is a streamlined version of PointTracker with a simplified interface and flexible model configurations. It tracks bright points (typically IR LEDs) arranged in 3, 4, or 5-point models, making it versatile for different hardware setups.
## How It Works
Easy Tracker detects bright blobs in the camera image and matches them to a predefined 3D point model. Using OpenCV's PnP (Perspective-n-Point) solver, it calculates the head's 6DOF pose. The tracker includes a Kalman filter for smooth, jitter-free output.
Easy Tracker offers multiple PnP solver options and supports custom 3, 4, and 5-point models, making it more flexible than the standard PointTracker.
## Requirements
### Hardware
* **Camera**: PS3 Eye camera or webcam with IR filter removed
* **IR LEDs**: 3-5 infrared LEDs (850nm or 940nm)
* **Power source**: Battery pack for LEDs (3-5V)
* **LED mounting**: Cap, clip, or headset for LEDs
### Software
* OpenCV (included with OpenTrack)
* Camera drivers
Same hardware requirements as PointTracker, but with more flexibility in LED arrangement.
## Setup Instructions
Easy Tracker supports multiple LED configurations:
* **3-point model**: Standard triangle (cap or clip style)
* **4-point model**: Quad configuration for better stability
* **5-point model**: Maximum accuracy and rotation range
* **Clip model**: Specialized 3-point clip configuration
Select your model type in tracker settings.
Create your LED array according to chosen model:
**For 3-point model:**
* Arrange 3 LEDs in asymmetric triangle
* Measure positions relative to center point
* Record X, Y, Z coordinates in millimeters
**For 4-point model:**
* Arrange 4 LEDs with at least one out of plane
* Ensure no 3 points are collinear
* Measure all positions
**For 5-point model:**
* Create extended configuration
* Use widely spaced points for best results
* Measure positions carefully
Set camera parameters:
```yaml theme={null}
Camera: Select your device
Resolution X: 640
Resolution Y: 480
FPS: 30-60
Field of View: 56 degrees
```
Adjust blob detection for your LEDs:
```yaml theme={null}
Min Blob Size: 4 pixels
Max Blob Size: 15 pixels
```
View the preview to verify only LEDs are detected.
Input your measured LED positions:
**For 3-point custom:**
```yaml theme={null}
Vertex Top (X, Y, Z): e.g., (0, 50, 0)
Vertex Right (X, Y, Z): e.g., (40, -30, -20)
Vertex Left (X, Y, Z): e.g., (-40, -30, -20)
```
**For 4-point:**
```yaml theme={null}
Vertex Top (X, Y, Z)
Vertex Right (X, Y, Z)
Vertex Left (X, Y, Z)
Vertex Center (X, Y, Z)
```
Coordinates are in millimeters, origin is at one reference point.
Choose pose estimation algorithm:
* **SOLVEPNP\_P3P**: Fast, requires exactly 3 points
* **SOLVEPNP\_ITERATIVE**: Robust, works with any number of points
* **SOLVEPNP\_EPNP**: Efficient for 4+ points
* **SOLVEPNP\_IPPE**: Good for planar configurations
Default P3P works well for 3-point models.
Set deadzone for steady aiming:
```yaml theme={null}
Deadzone Half Edge Size: 1-5 pixels
```
Larger values reduce jitter but may feel less responsive.
Configure automatic centering:
```yaml theme={null}
Auto Center: true
Auto Center Timeout: 1000ms
```
Automatically centers view when you stop moving for the timeout period.
## Configuration Options
### Camera Settings
| Option | Default | Description |
| ---------------- | ------- | -------------------- |
| **Camera Name** | - | Select camera device |
| **Resolution X** | 640 | Width in pixels |
| **Resolution Y** | 480 | Height in pixels |
| **FPS** | 30 | Target framerate |
| **FOV** | 56° | Camera field of view |
### Blob Detection
| Option | Default | Description |
| ----------------- | ------- | ------------------------------- |
| **Min Blob Size** | 4 | Minimum blob diameter in pixels |
| **Max Blob Size** | 15 | Maximum blob diameter in pixels |
### Model Configuration
| Option | Default | Description |
| ---------------------- | ------- | ------------------------------- |
| **Custom Model Three** | true | Enable 3-point custom model |
| **Custom Model Four** | false | Enable 4-point model |
| **Custom Model Five** | false | Enable 5-point model |
| **Clip Model Three** | false | Enable clip-style 3-point model |
### Model Vertices (Example for 3-point)
| Vertex | X | Y | Z | Description |
| --------- | - | - | - | ---------------------- |
| **Top** | 0 | 0 | 0 | Top/front LED position |
| **Right** | 0 | 0 | 0 | Right LED position |
| **Left** | 0 | 0 | 0 | Left LED position |
All coordinates in millimeters from origin point.
### Solver Settings
| Option | Default | Description |
| ----------------------- | ------------- | -------------------------------- |
| **PnP Solver** | SOLVEPNP\_P3P | Pose estimation algorithm |
| **Deadzone Half Edge** | 1 | Deadzone size in pixels |
| **Auto Center** | true | Enable automatic centering |
| **Auto Center Timeout** | 1000ms | Time before auto-center triggers |
| **Debug** | false | Show debug information |
| Solver | Points | Speed | Accuracy | Use Case |
| --------- | --------- | --------- | --------- | ------------------------- |
| P3P | Exactly 3 | Very Fast | Good | Standard 3-point tracking |
| ITERATIVE | Any | Medium | Good | General purpose |
| EPNP | 4+ | Fast | Very Good | 4 or 5 point models |
| IPPE | 4+ | Fast | Good | Planar arrangements |
| SQPNP | Any | Medium | Very Good | When accuracy critical |
## Advanced Features
### Kalman Filter
Easy Tracker includes a built-in Kalman filter that:
* Smooths position and rotation outputs
* Predicts pose during brief point occlusions
* Reduces jitter from blob detection noise
* Automatically adapts to movement speed
The filter is always enabled and tuned for responsive yet smooth tracking.
### Multiple Point Models
Support for different point counts provides flexibility:
**3-point models:**
* Simplest to build
* Lowest computational cost
* Good for most use cases
* May have pose ambiguity at extreme angles
**4-point models:**
* Eliminates most pose ambiguity
* More robust tracking
* Better for extreme head rotations
* Slightly more complex to build
**5-point models:**
* Maximum tracking stability
* Best rotation range
* Most robust to point occlusion
* Requires careful LED placement
### Vertex Matching
The tracker uses sophisticated vertex matching to identify which detected blob corresponds to which model point:
1. **Distance-based matching**: Compares blob positions to expected positions
2. **Geometry validation**: Ensures detected points form valid configuration
3. **Temporal consistency**: Uses previous frame to resolve ambiguities
4. **Fallback strategies**: Multiple matching algorithms for robustness
## Troubleshooting
* Adjust min/max blob size to filter out false detections
* Check for reflections in camera view
* Increase LED brightness relative to background
* Reduce threshold if LEDs too dim
* Block windows/lights causing reflections
* Verify model coordinates are accurate
* Check that model is asymmetric (not symmetric triangle)
* Try different PnP solver (ITERATIVE more robust)
* Increase blob size constraints
* Ensure LEDs are rigidly mounted
* Increase deadzone size
* Verify LEDs are securely mounted
* Check for camera focus issues
* Use higher camera resolution
* Ensure consistent LED brightness
* Check mounting stability
* Verify at least minimum points detected (3 for P3P)
* Check model coordinates are entered correctly
* Try different PnP solver
* Ensure model points aren't collinear
* Check that model scale is reasonable
* Verify auto-center is enabled
* Check timeout setting (try increasing)
* Ensure you're actually stopping movement
* Debug mode shows center status
## Performance Tips
* Use P3P solver for 3-point models (fastest)
* Lower camera resolution (640x480 sufficient)
* Reduce FPS if CPU limited
* Minimize blob size range
* Use 4 or 5-point model
* Increase camera resolution
* Larger LED spacing in model
* Use EPNP or SQPNP solver
* Increase deadzone size
* Use 4+ point model
* Ensure rigid LED mounting
* Use consistent LED brightness
* Use 5-point model
* Space LEDs widely
* Use ITERATIVE solver
* Increase max blob size
## Comparison with PointTracker
| Feature | Easy Tracker | PointTracker |
| ---------------- | --------------- | ------------ |
| Point models | 3, 4, or 5 | 3 only |
| PnP solvers | 5+ options | Fixed |
| Kalman filter | Always on | Optional |
| Auto-center | Built-in | No |
| Setup complexity | Slightly easier | Standard |
| Performance | Very similar | Very similar |
| Flexibility | More flexible | More mature |
## Tips for Best Results
1. **Model Design**: Use asymmetric arrangements - avoid regular triangles or rectangles
2. **LED Spacing**: Wider spacing = better accuracy, aim for 80-120mm between points
3. **Solver Selection**: Start with P3P, switch to ITERATIVE if you get pose flips
4. **Calibration**: Measure LED positions accurately - 1mm error affects results
5. **Mounting**: Ensure LEDs don't flex or move relative to each other
6. **Testing**: Use debug mode to verify point detection before finalizing setup
## See Also
* [PointTracker](/trackers/pointtracker) - Alternative 3-point tracker
* [Hardware Guide](/trackers/hardware) - Building LED arrays
* [Wiimote Tracker](/trackers/wiimote) - Alternative IR tracking hardware