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 2018 Southern African Transport Conference

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…

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

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

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

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

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

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

Experiment 1: Mathematical Simulation

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

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

Experiment 2: Geometric Test

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

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

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

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.

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