4936481-adaboost-plate

							Learning-based License Plate Detection on Edge Features

Presenter: Ho Wing Teng Authors: Mr. Ho Wing Teng, Mr. Yap Wooi Hen, Dr. Tay Yong Haur Computer Vision and Intelligent Systems (CVIS) Group Universiti Tunku Abdul Rahman, Malaysia

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Introduction
Presents license plate detection in unconstrained environment (cluttered scenes, changing illumination, in-plane and out-plane rotation of license plates) Adaboost learning-based method is implemented by Viola & Jones in their face detector.
[Viola, P. & Jones, M. (2004). Robust Real-Time Face Detection. International Journal of Computer Vision 57(2), 137-154.]

We present comparative results of our approach against two experiments:
– –

License plate image Intensity value. License plate image with edge detection

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LPD System Architecture

Original Image Convert to gray-scale image Canny Edge-detection License Plate Detector Result

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Image Pre-processing
Edge Detection – pre-process the original image before the detection – An edge represents the boundary of an object – used to identify the shapes and area of the particular object – remove some of the noises and make the license plate text edges more visible
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Canny Edge Detection (cont) Canny Edge Detection – Canny edge detector is one of the most popular edge detection methods – it provides optimal detection with no false detection, – better localization with minimum difference within the actual edge position and the detected edge

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Examples
Artificial License Plate without using canny edge detection.

Artificial license plate using canny edge detection.
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Test Samples

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Viola & Jones Face Detection Architecture
Adaboost Learning Algorithm
Training Samples Feature Selection Features Cascading Final Classifier

Haar-like Features

1

T F

2

T F

3

T F

N

T F

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Feature Types used in Face Detection by Viola & Jones Haar-like Features

summation within the black region and from the pixels summation within the white region

• Feature can be computed from the subtraction of pixels
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Haar-like Features 384

288
Resolution Size

45 12

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White Region Black Region • Feature can be computed

from the subtraction of pixels summation within the black region and from the pixels summation within the white region

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Haar-like Features

• Variant of Features available to the adaboost training

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Schematic diagram of a 4 stages cascaded classifiers

Every processing node is a strong classifier Initially, large number of negative windows is rejected with very small processing time. Subsequent nodes eliminate additional negative windows with additional UNIVERSITI TUNKU ABDUL RAHMAN processing time

Result & Discussion

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Result & Discussion

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Conclusion and Future Works
Proposed Adaboost learning based method to construct LPD on Haar-like features Edge information has demonstrated better discriminative power compared to intensity information The total time costs only ~ 80 ms to process an image of 320 x 240 further improvement is necessary to reduce false alarm rate as well as increasing true positives
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How to calculate Integral Image
In the example to calculate the sum pixels in D, we need to determine the integral image of A, B, C, and D which is represented in number 1,2,3,and 4 in figure 3.5. Number 1 is the total of the pixel values in area A; Number 2 is the total of the pixels in area A and area B; Number 3 is the sum of the pixels in area A and area C; at last the number 4 will be the entire sum of the whole image which is the total of A+B+C+D. Therefore to get the total pixel in D, we can calculate with 4+1-(2+3).

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Formulae to calculate number of features available for adaboost training

Formula calculate number of features for an image size w x h

Figure 3.4 Formula calculate number of rotated features for an image size w x h

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•Table adopted from viola paper summarize the boosting algorithm for learning a query online.

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AdaBoost Training Process Diagram
Training Samples Positive Samples Negative Samples Assign to Training Samples with weights Start training Generate Large Feature Set

Calculate feature value on each training samples

Haar-like Feature

Update the weight in Training Samples yes The error < maxError

Calculate the Error value for each features Determine Threshold values Select the feature with lowest error Save the Selected feature To a text file

End training

No

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