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Automatic License Plate Location Using Template Matching University of Wisconsin - Madison ECE 533 Image Processing Fall 2004 Project Kerry Widder Slide 2 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 Problem Statement Slide 3 Distance sets (spatial arrangement of features) Morphological operations Contrast levels Contrast transitions row, column Genetic algorithms Pattern matching Approaches Slide 4 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. Approach Slide 5 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 Implementation Algorithm Slide 6 Obtained ten images of vehicles Conditions were controlled: sunny, same distance All images were processed through the algorithm implemented in MATLAB Implementation Data Slide 7 Implementation Data sample of processed data Input image Edge image Correlation result Boundaries Slide 8 Implementation Data Templates License plate template Correlation template Slide 9 Results Correlation - success Sobel image Best matches marked Correlation result Correlation on correlation Slide 10 Results Correlation - failure Correlation image Best matches marked Slide 11 Results Moment Invariants - success Input image Boundaries Selected match Slide 12 Results Moment Invariants - failure Input image Boundaries - insufficient Input image Boundaries - protrusions Slide 13 Results Summary Correlation 60% success rate Correlation on correlation 20% success rate Moment Invariants 20% success rate Slide 14 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 Slide 15 Conclusion Simple template matching, using correlation or moment invariants, does not appear to be a good candidate for automatic license plate location.