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Automatic License Plate Location Using Template Matching University of Wisconsin - Madison ECE 533 Image Processing Fall PowerPoint Presentation
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Automatic License Plate Location Using Template Matching University of Wisconsin - Madison ECE 533 Image Processing Fall 2004 Project Kerry Widder. Problem Statement. What: Automatically locate a license plate in the image of a vehicle Who: Law enforcement, parking structures

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Automatic License Plate Location Using Template Matching University of Wisconsin - Madison ECE 533 Image Processing Fall


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Presentation Transcript
slide1

Automatic License Plate Location Using Template Matching

University of Wisconsin - Madison

ECE 533 Image Processing

Fall 2004 Project

Kerry Widder

slide2

Problem Statement

What: Automatically locate a license plate in the image of a vehicle

Who: Law enforcement, parking structures

Why: Quicker, cheaper

Difficulties: variations – lighting, angle, size, location, distance, color, vehicle features, contrast, holders

slide3

Approaches

Distance sets (spatial arrangement of features)

Morphological operations

Contrast levels

Contrast transitions – row, column

Genetic algorithms

Pattern matching

slide4

Approach

Approach used in this project – template matching

Two methods used to measure a template match:

1. Correlation

The greater the similarity between the template and the image in a particular location, the greater the value resulting from the correlation.

2. Moment Invariants

A set of regional statistical descriptors – invariant to translation, rotation and scale changes.

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Implementation

Algorithm

Filter image (gaussian, then Sobel to find edges)

Calculate correlation and correlation on correlation

Threshold sobel image to convert to binary

Perform Morphological opening to reduce protrusions

Find boundaries in binary image

Calculate moment invariants of each object and compare to template to find best match

slide6

Implementation

Data

Obtained ten images of vehicles

Conditions were controlled: sunny, same distance

All images were processed through the algorithm implemented in MATLAB

implementation data sample of processed data
ImplementationData – sample of processed data

Input image Edge image

Correlation result Boundaries

implementation data templates
ImplementationData – Templates

License plate template Correlation template

results correlation success
ResultsCorrelation - success

Sobel image Best matches marked

Correlation result Correlation on correlation

results correlation failure
ResultsCorrelation - failure

Correlation image Best matches marked

Correlation image Best matches marked

results moment invariants success
ResultsMoment Invariants - success

Input image Boundaries

Selected match

results moment invariants failure
ResultsMoment Invariants - failure

Input image Boundaries - insufficient

Input image Boundaries - protrusions

results summary
ResultsSummary

Correlation

60% success rate

Correlation on correlation

20% success rate

Moment Invariants

20% success rate

discussion
Discussion

Correlation: moderate success, correlation on correlation not better

Further work – refine template? (will be difficult to do for all cases)

Moment Invariants: not successful

Further work – refine boundary/object identification (difficult due to wide variations in vehicle features, lighting, etc.)

Limitations: sample size small, images controlled

conclusion
Conclusion

Simple template matching, using correlation or moment invariants, does not appear to be a good candidate for automatic license plate location.