license plate identification amir ali ahmadi jonathan neville justin sobota mehmet ucal

License Plate IdentificationLicense Plate Identification

Amir Ali AhmadiJonathan Neville

Justin SobotaMehmet Ucal

Outline

• Motivation

• Previous Work

• Approach

• Algorithms– Character Identification– Plate Extraction

• Results

• Conclusion/Future Work

Motivation

• Traffic Control

• Automated Ticketing

• Finding Stolen Cars

• High Speed Pursuit

Previous Work

• License Plate Identification/Recognition (LPI/R)– http://www.photocop.com/– Retrieves Plate Numbers for All States– Determines Speed– Several vendors

• Three algorithms for license number extraction

Previous Work

• Template Matching– Compares extracted characters to a set of

templates– Very reliable under standard conditions– Viewing angle, Lighting, plate size, etc. can

cause errors

Previous Work

• Structural Analysis– Uses geometric features and a decision tree to

determine character

– Very complex time-consuming analysis

Loops?# of Loops

Location of Loop?

Left Side Straight?B

8

yes

1

2 yes

no

no

top

bottom

middle

6

D

Previous Work

• Neural Networks– Trained by example– Adapt to characters’ distinctive feature– Performs well in bad conditions

Our Approach

• Template Matching

• Assumptions– Only white Maryland Plates– Camera angle directly behind car– 2 types of MD plates

• 6 characters with MD logo in center• 7 characters

Approach

Plate Extraction

Character Extraction

Template Matching

CharacterIdentification

Character Identification

Char. Extract

Support Set Extract

Comparison

Char.Filtering

TemplateFiltering

TemplateImages

LicensePlate

PlateNumber

Template Filtering

• Templates obtained from actual plates• Template Filtering

– RGB2Gray– Threshold (Black/White)– Resize

• Output array of templates

Character Extraction

• Plate resized to predetermined dimensions• Output array of extracted characters

Character Filtering

• RGB2Gray• Threshold (Black/White)• Median Filtering

Character Identification

Char. Extract

Support Set Extract

Comparison

Char.Filtering

TemplateFiltering

TemplateImages

LicensePlate

PlateNumber

Support Set Extraction

• Row sums• Column sums• Exclude low sums• Extract largest

continuous region• Resize to

template size

Comparison

?

?

Approach

Plate Extraction

Character Extraction

Template Matching

CharacterIdentification

Plate Extraction

• RGB2Gray• Threshold(Black/White)

• Row/Columnmeans

• Extract largestcontinuous whiteregion

Results for Character Identification

Input OutputLicense Identification

License Identification

License Identification

Results for Character Identification

Input OutputLicense Identification

License Identification

Results for Plate Extraction

Input OutputPlate Extraction

Results for Plate Extraction

Input OutputExtracted “M”

Failed Plate Extractions

Input OutputPlate Extraction

Failed Plate Extractions

Input OutputPlate Extraction

No Extracted Plate

No Extracted Plate

No Output

No Output

Conclusion

• Template matching approach was taken• Algorithm

– Plate Extraction– Character Identification

• Given the plates, we were able to identify almost all of the characters

• Plate extraction was limited to darker cars

Future Work

• Improve templates to better accommodate the plate characters

• Refine threshold levels for determining the whiteness in the picture

• Eliminate issues regarding glare, dirtiness of the plate, shadows, and white regions in the picture

• Dynamic character extraction – Character position found by the algorithm

Demonstration