License Plate Identification

1. License Plate Identification Amir Ali Ahmadi Jonathan Neville Justin Sobota Mehmet Ucal

2. Outline • Motivation • Previous Work • Approach • Algorithms • Character Identification • Plate Extraction • Results • Conclusion/Future Work

3. Motivation • Traffic Control • Automated Ticketing • Finding Stolen Cars • High Speed Pursuit

4. 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

5. 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

6. Previous Work • Structural Analysis • Uses geometric features and a decision tree to determine character • Very complex time-consuming analysis 6 bottom top Location of Loop? 1 middle yes # of Loops D Loops? 2 yes B no Left Side Straight? no 8

7. Previous Work • Neural Networks • Trained by example • Adapt to characters’ distinctive feature • Performs well in bad conditions

8. 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

9. Approach Plate Extraction Character Extraction Character Identification Template Matching

10. Character Identification Char. Extract License Plate Char. Filtering Support Set Extract Plate Number Comparison Template Images Template Filtering

11. Template Filtering • Templates obtained from actual plates • Template Filtering • RGB2Gray • Threshold (Black/White) • Resize • Output array of templates

12. Character Extraction • Plate resized to predetermined dimensions • Output array of extracted characters

13. Character Filtering • RGB2Gray • Threshold (Black/White) • Median Filtering

14. Character Identification Char. Extract License Plate Char. Filtering Support Set Extract Plate Number Comparison Template Images Template Filtering

15. Support Set Extraction • Row sums • Column sums • Exclude low sums • Extract largest continuous region • Resize totemplate size

16. Comparison ? ?

17. Approach Plate Extraction Character Extraction Character Identification Template Matching

18. Plate Extraction • RGB2Gray • Extract largestcontinuous whiteregion • Threshold(Black/White) • Row/Columnmeans

19. Results for Character Identification Input Output License Identification License Identification License Identification

20. Results for Character Identification Input Output License Identification License Identification

21. Results for Plate Extraction Input Plate Extraction Output

22. Results for Plate Extraction Input Extracted “M” Output

23. Failed Plate Extractions Input Plate Extraction Output

24. Failed Plate Extractions Input Plate Extraction Output No Output No Extracted Plate No Output No Extracted Plate

25. 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

26. 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

27. Demonstration