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PSYCH 221 Automatic License Plate Recognition

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PSYCH 221 Automatic License Plate Recognition

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    1. Max Guise Stephan Goupille PSYCH 221 Automatic License Plate Recognition

    2. Team Members Max Guise Electrical Engineering Technical Staff, Sandia National Labs Stephan Goupille MS&E, Electrical Engineering Growth Analyst, Facebook

    3. Project Aims

    4. Project Aims - Adapted Character recognition particularly difficult and requires a massive database of license plates Focus on license plate extraction:

    5. Tools Motorola Droid Phone Great for snapping a quick picture Originally thought RGB values could be exported directly (function hasn’t been implemented) MATLAB One of the best, fastest mathematical analysis tools Many built-in image processing functions

    6. Other ALPR Systems Most systems in actual use benefit from a controlled scenario: Lack of background objects Higher quality imaging Better flash (license plates are retroreflective) Minimal scaling and rotation Ease of access to large database from which to build a model Typically use a machine learning algorithm (SVM, LDA, etc.) to make final decisions Our goal to accomplish task using basic properties of license plates – no machine learning necessary

    7. Methods Pre-Processing KMeans Adaptive Histogram Equalization Image Transformation Histogram of Oriented Gradients Harris Corners Hough Transform Edge Detection Filtering Decision-Making Thresholding Erosion

    8. KMeans Unsupervised Nearest Centroids in 5-Space (X,Y,R,G,B) Variable number of centers can be chosen Can weight X,Y differently from R, G, B Goal: License plate shows up as one block

    9. KMeans Issues: Optimal number of centers to use varies with size of license plate As a result, can’t guarantee good separation of plate from bumper, or might split license plate region into multiple parts Can be computationally expensive – either in itself or later in the pipeline

    10. Histogram Equalization Image pixels may be far from uniformly spread across the display range Changing the distribution can add contrast Can be done across whole image, or ‘adaptively’ Tiling across image might split up an individual license plate region

    11. Histogram Equalization

    12. Histogram Equalization

    13. Histogram Equalization

    14. Histogram of Oriented Gradients

    15. Harris Corner Detector

    16. Hough/Radon Transform

    17. Hough/Radon Transform

    18. Simplest is best

    19. Horizontal Gradient

    20. Matched Filtering

    21. Threshold, Erode, Label

    22. False Positive Rejection

    23. Conclusions

    24. References

    25. Questions?

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