Camera Mounting Requirements¶
🎯 Camera Mounting Height¶
- Minimum installation height: 4 meters
- Recommended height:
- 4–5 m for standard cross-sections and smaller intersections
- 6–7 m for large intersections and roundabouts with multiple lanes
📌 Why is this important?¶
- Camera mounted too low → further lanes obscured by vehicles closer to the lens.
- Such occlusions are difficult for AI to resolve correctly.
- The higher mount reduces obstruction problems and increases the field of view.
Quick Reference Table¶
| Scenario | Height | Pitch Angle | Notes |
|---|---|---|---|
| Cross-section | 4 m | 15°–25° | Camera parallel to the road |
| Road intersection | 5–7 m | 30°–45° | Camera right next to the intersection |
| Roundabout | 5–7 m | 30°–45° | Camera right next to the roundabout |
📐 Pitch Angle and Field of View (FOV)¶
Road Cross-section¶
- Tilt (Pitch): 15°–25°
- Horizontal FOV: 60°–80°
- Alignment: Parallel to the road axis – optimal for License Plate Recognition (LPR) and capturing vehicle side profiles.
Intersections¶
- Tilt (Pitch): 30°–45° (can be increased to 45°–60° for large interchanges)
- Horizontal FOV: 90°–120°
- Note: The closer to vertical (Pitch → 90°, i.e., "bird's-eye view"), the better the visibility of traffic relations and the fewer occlusions.
Roundabouts¶
- Tilt (Pitch): 60°–90° (optimal: vertical, Pitch ≈ 90°)
- Horizontal FOV: 100°–130°
- Alignment: Central mounting above the roundabout (if possible) or multiple cameras at entries – each with its own specific angle.
Optimal Camera Framing for Traffic Flow Counting and ANPR¶
An example of a correct frame for a road intersection.
Example of a correct frame for a road cross-section.
🛣️ Mounting Location¶
One-way road / Cross-section¶
- Pole-mounted by the road
- Camera parallel to the road axis
- Digital cropping available in AITracker if the camera is positioned away from the road
Intersections¶
- Mounted on a pole directly at the intersection
- Large intersections: 1 camera per entry
Roundabouts¶
- Mounted on a pole directly at the roundabout
Correct Camera Frame Examples¶
Intersection or Roundabout Counting¶
Example of a correct frame for an intersection.
Sectional Speed Measurement¶
Correctly defined speed measurement section.
ALPR for Parking Time Measurement¶
A camera mounted inside a vehicle traveling on the road and counting traffic.
🌙 Night Measurement and Weather Conditions¶
- Use cameras with IR (Infrared), especially in unlit areas.
- Rain, fog, or dirt on the lens → although the AI is trained on such footage, severe weather conditions can still reduce analysis effectiveness.
⚠️ Common Mounting Errors¶
| Error | Consequence |
|---|---|
| Mounting too low | Occlusions caused by vehicles in the foreground |
| Incomplete coverage of the intersection/roundabout | Failure to count entries that are outside the frame |
| Trees in the middle of a roundabout | Interrupted tracking trajectories |
| "Face-on" camera alignment instead of an angled view | Causes additional occlusions |
| Frame too narrow or insufficient resolution | Causes AI errors or failure to recognize objects |
💡 How Does Night Lighting Work with the Cameras?¶
| Scenario | AI Recognition |
|---|---|
| ✅ A visible vehicle silhouette | Ensures AI recognition |
| ✅ A partially visible vehicle silhouette | Sufficient for AI |
| ❌ A vehicle as a ball of light | Will NOT be recognized |
| ❌ Vehicles with only headlights visible | Insufficient for AI |
| ⚠️ High speed and significant blur | Borderline recognizable |
🧩 Example Problems and Solutions¶
Problem: "The camera cannot see the second lane; it is obstructed by a truck."¶
✅ Solution: Increase the mounting height or add a second camera.
Problem: "ALPR fails to recognize plates at dusk."¶
✅ Solution: Reduce the camera's shutter speed or increase the FPS (frames per second). Both solutions improve image sharpness.
Problem: "ALPR fails to recognize plates at night."¶
✅ Solution: The camera must be equipped with IR illumination, and the light's angle of incidence should be 45 degrees.