1. Factors that Influence the Geometric Detection Pattern of Vehicle-based Licence Plate Recognition Systems
Martin Rademeyer Thinus Booysen, Arno Barnard
Department of Electrical & Electronic Engineering Stellenbosch University
11 July Southern African Transport Conference

2. Introduction License Plate Recognition (LPR) Static mounted system
Uses: Speed cameras, E-tolls
Static mounted system
Monitor a corridor bottleneck or crucial intersection
Vehicle mounted system
Large scale dynamic surveillance covering extensive road network
Many additional variables…

3. Geometric Detection Pattern
Region in front of the vehicle in which license plates can accurately be recognised
Under ideal environmental conditions
Static
Constant lighting
Commercial standard is 8 to 12 meters maximum range
Highly dependant on the camera specifications and recognition algorithm

4. Comprehensive Optics Overview
License plate reflects light towards the camera
Camera lens concentrates incoming light onto the image sensor
Image sensor is an array of photo- sensors used to capture image electronically
Camera lens can be adjusted to focus on subjects at different distances

5. License Plate Recognition Software
Many propriety and free software available
Independent of camera used in system
Algorithm pipeline stages
License plate detection
Character separation
Character identification
Common algorithm limitations
Insufficient resolution
Skewed view of license plate
Camera distorts license plate

6. Geometric Detection Pattern Metrics
Angle of View
The solid angle describing the extent of the scene captured
Viewing Angle
The maximum angle at view a license plate can be captured and recognised
Depth of Field
The region within which the license plate will be captured in sufficient focus
Critical Apparent Angle
The minimum apparent angle of the plate

7. Camera specifications producing GDP
Focal Length
Sensor Resolution
Sensor Size
Lens Distance
Aperture Size
Angle of View
In-focus Distance
Hyper-focal
Depth of Field
Critical Ap Angle
Viewing Angle

8. Experiment 1: Mathematical Simulation
Multi-dimensional problem with camera specifications each varied over a spectrum to evaluate every combination
Each combination produces GPD properties
Use case positions spaced along virtual test range
Produce a visual representation of the GPD
Considerations
Adjacent lanes
Static geometric analysis
Reasonable spec combinations

9. Experiment 1: Mathematical Simulation

10. Experiment 2: Geometric Test
Test system: Camera, Pi, Laptop
Camera selected based on simulation
8MP 1/3.2“ sensor, 3-12mm focal, f/1.8
Some specs manually adjustable
Raspberry Pi micro-computer
Python script for image capture
OpenALPR recognition algorithm
Laptop with remote connection
Control test and store results

11. Experiment 2: Geometric Test
Test range
3 lane, 40 meters
Positions distributed
EU license plates
Controlled lighting
Static
Procedure
Recognition accuracy
Various positions
Various spec settings

12. Experiment 2: Geometric Test

13. Results: Mathematical Simulation
Insight into how individual specifications influence GDP
Varied focal length cause GDP area to fluctuate, with max area at 14mm
Dependent on an in-focus distance above 18 meters
Short focal length gives wide AoV but increases CAA.
Aperture has negligible effect on Depth of Field

14. Results: Geometric Test
Recognition fails at less than 4000 pixels per license plate. Range limiter. Despite same threshold, shorter range than simulation.
Maximum viewing angle of 45 degrees
Insensitivity to focus
Actual depth of field much greater
Single focus setting is sufficient for entire GDP
Barrel distortion on wide angle lens
Only with very wide angle, very close range

15. Results Focal length Can set between long or wide
Long providing greater area
But missing near vehicles
Diagram Long: 12mm Near: 3mm
a) Simulation b) Geometric Test

16. Conclusion
Focal length: By far the most influential specification. Largely determines the angle of view and the effective range of the GDP, allowing choice between long and wide GDP. A constant focal length can result in sufficient GDP area for vehicle-based LPR.
In-focus distance: A constant setting allowed focused capture across entire GDP. Algorithm allows for some fuzziness.
Aperture Size: Minimal effect on the GDP, expected to have impact on light sensitivity and motion blur.
Image Sensor: High resolution greatly extends GDP range, with the coupled large size having negligible effect. Sensor resolution is responsible for determining the maximum range boundary.

17. Thank you