A real time license plate detection system based on ...

2012 5th International Congress on Image and Signal Processing (CISP 2012)

A real time license plate detection system based on Boosting learning algorithm Thanh-Tung NGUYEN

Thuy Thi NGUYEN

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China 518055 Water Resource University, Hanoi, Vietnam

Hanoi University of Agriculture Hanoi, Vietnam

Abstract—Boosting is one of the most well-known and effective techniques in machine learning. The success of using boosting for training a face detector [28] has paved the way of using boosting for training object detectors and made it widely used in computer vision. In this work we present a new framework for fast and automatic detection of vehicle license plate based on boosting learning algorithm. Beside the traditional Haar-like features, we propose to use local binary pattern (LBP) feature for its robust and discriminative power. The boosting classifiers are trained on these features and then combined in an efficient way to achieve high performance. An intensive set of experiments have been conducted. The results show that the classifier with LBP outperform that of Haar-like in the same scenario for the license plate detection problem. By combining them in an reasonable way, our proposed system can perform in real time for detection of license plates with the accuracy up to 100%, outperform stateof-the-art approaches. Keywords-Classification; Adaboost; Real time object detection; License plate detection.

I.

INTRODUCTION

In modern world, with the increasing number of vehicles in transport systems, how to manage them in an efficient way and how to build reliable Intelligent transportation system (ITS) are remain big problems. For those problems, license plate is still principle vehicle identifier and therefore the main object to study. To automatically manage the vehicles, a system for automatic recognition of license plate is necessary. An automatic License Plate Recognition (LPR) system has various real life applications, such as automatically recognizing vehicles for intelligent parking, automated toll systems, detecting and verifying stolen vehicles, etc. There have been a lots of researches which lead to some well-known commercial LPR systems around the world [1] [2]. A typical LPR system consists of two major components: license plate detection (localization) and license plate recognition. Wherein, license plate detection and localization is an important step for the performance of the whole system. This work focuses on the first stage, license plate (LP) detection. That is to localize the license plate in an image and specify it by a bounding box, which covers the license plate area. This is a crucial step in the automatic license plate recognition technology, and is a very challenging task in the computer vision and image processing. The difficulties are from multiple aspects, such as the variances of LP appearances, lighting condition, background

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environment, etc. Researchers have been interested in the problem for decades [6], [9], [4], [3], [21], [30]. Recently, with the success of a machine learning technique named boosting for the face detector, boosting has been employed for LP detection. However, most of works that used boosting for LPD in literature still just use a simple version of boosting with the traditional Haar-like features. To the best of our knowledge, there is no attempt to combine those features at (either at feature or at) classifier level to improve the performance. In this work, we develop a LPD system for automatic detection of LP in real time. We propose to use Boosting - a fast and efficient algorithm - for learning a license plate detector. We use integral image for fast computing of features for learning. Beside the traditional Haar-like features, we employ Local binary pattern (LBP) for discriminative representation of license plate image patch. The employment of LBP is reasonable as we can observe that, in a LP the background of text is usually white or color with very fine texture, and the area of the background is larger than total area covered by texts. This property makes it possible to use textural feature (LPB) distinguish the LP area from surroundings. Moreover, we present a mechanism for removing of false positive, and thus improve the accuracy of the system, by combining detections at classifier level. Our paper is organized as follows: section II is for related works. Our system is presented in section III. Section IV is for experiment, results and discussion. Finally, conclusion and future work will be included in section V. II.

RELATED WORK

The problem of LPD has drawn the attention of researchers for decades. There have been attempts to solve the problem in the field of Computer vision, image processing, and artificial intelligence. In general, approaches for solving the problem can be categorized into three groups, namely edge-based, segmentation-based, and learning-based methods. The edge-based methods have been one of the most popular approaches [17], [11], [33], [30]. They based on the property that brightness changed in the license plate region is more than other locations. Techniques for these approaches are based on combinations of edge statistics and mathematical morphology. In the segmentation-based approaches [6], [21], [23], the most important factor is the segmentation stage based on color or gray scale images. Since these techniques are mainly color-

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based, they have difficulties recognizing a license plate image if the image has many similar parts of color values to a plate region, and fail at detecting license plates with different colors. Recently, machine learning methods have become popular in computer vision in general, and in License plate detection, in particular. This approaches have demonstrated to be efficient and robust. Early time, Artificial neural network (ANN) has been employed [9], [4], [13]. But, the main problems of these approaches are the mystery of designing network architectures and computational expensive. In [12], SVM has been used for learning to analyze the color and textural properties of license plates in images. To reduce computing time, only a small part of the input image to be analyzed. In [8], Adaboost (Adaptive Boosting-AB) learning has been used for detection of license plates. Very recently, there have been increasing research interest in using boosting techniques in license plate detection and recognition [32], [5], [24], [27]. In [32], the Adaboost algorithm was used for the license plate detection, in which both global features and local Haar features were adopted to detect license plates. These features and the cascade structure together selected 6 strong classifiers in all 160 weak classifiers, and its detection rate reached 93.6%. In addition to the progress of the license plate detection rate, the license plate detection processing speed was radically improved due to the use of the cascade structure. In [5] AB algorithm was also used to detect license plates in the lowquality license plate video. The detection rate reached 98.9% whereas better real-time performance and robustness were maintained. Recently the license plate detection based on the AB algorithm and an auto-correlation feature was reported in [25], [24] shows an empirical analysis of three boosting algorithms for LPD, which are Discrete AB, Real AB and Gentle AB. The study shows that the GAB algorithm used in the license plate detection system is better that the DAB or RAB algorithm. This result provides a practical engineering guide to the automatic license plate detection technology. However, we found that in all related works, up to our knowledge, there was no attempt to use boosting with Local binary pattern as discriminative features, and no investigation on how to combine detection results at classifiers level for further improving detection rate. In this work we propose to use boosting approach for learning a LP detector. In our framework, beside the traditional Haar-like feature, we employ the LBP feature for its discriminative power. We also propose a mechanism for combining classifiers for improving accuracy. III.

THE LICENSE PLATE DETECTION SYSTEM

Boosting learning algorithm Boosting is one of the most well-known ensemble learning methods with well-studied theory and strong supporting experimental results. In general, boosting improves (boosts) the performance of any classifier into a strong one by linear combination of the base (weak) classifiers. With a smart combination, a boosted strong classifier often outperforms most 'monolithic' strong classifiers such as Neural Networks or Support Vector Machines. The algorithm has been analyzed carefully and tested empirically by many researchers in the community, e.g. [22], [14]. Various variants of Boosting have been developed, e.g. Real-Boost [10], LP-Boost [7].

(a) Haar-like

binary classification the error rate must be less than 50%. A hypothesis generated by a weak learner is called weak hypothesis and denoted as h weak (x) ; A strong classifier: Given a set of N weak classifiers, a strong classifier is produced by linear combination of the base classifiers.

The discrete AB algorithm was first introduced in [10]. The algorithm adaptively re-weights the training samples instead of re-sampling them. The basic algorithm works as follows: Given a training set X = {(x1 , y1 ),....(x L , y L ) | xi ∈ R m , y i ∈ {− 1,+1} } with positive and negative labeled samples and an initial uniform distribution p( x) = 1 over the examples. L

Based on X and p(x), a weak classifier h weak is trained. The classifier is obtained by applying a learning algorithm, e.g. applying statistical learning for a decision stump. Based on the error en on training data X, i.e: L

en =

∑p

n ( xi ) hn ( xi ) yi

(1)

i =1

the weak classifier

hn

weak

gets assigned a weight

⎛ 1 − en ⎞ 1 ⎟⎟ ⋅ (2) ⋅ ln⎜⎜ 2 ⎝ en ⎠ The probability p(x) is updated such that it increases for the samples that are misclassified. The corresponding weight is decreased if the sample is classified correctly. Therefore, the algorithm focuses on the difficult examples. At each boosting iteration a new weak classifier is added and the process is repeated until a certain stopping condition is met. Finally, a strong classifier h strong (x) is computed as linear combination of a set of N weak classifiers hnweak (x) :

αn =

h strong ( x) = sign(conf ( x)) (3)

A.

In boosting, a weak classifier is a learning algorithm that needs to perform just better than random guessing, i.e. for the

(b) LBP pattern

Figure 1. Basic image features used. (a) The value of the Haar-like feature is the difference of the pixel values between the white and the black marked region. (b) Simple version to obtain a local binary pattern value (LBP).

conf ( x ) =

∑

N n =1

α n ⋅ hnweak ( x)

∑

N n =1

(4)

αn

As conf (x) is bounded by [-1, 1], it can be interpreted as a confidence measure. The higher the absolute value is the more confident is the detection. Freund and Schapire [10] proved strong bounds on the training and generalization error of AdaBoost. For the case of binary classification, the training error drops exponentially fast with respect to the number of boosting rounds N, i.e., number of weak classifiers. [22], [20] showed that boosting maximizes the margin and proved that larger margins for the training set are translated to superior upper bounds on the generalization error. 1)

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Boosting for feature selection:

Figure 2. Efficient calculation of the sum over a rectangular area. The value of the integral image at Position P1 is the sum of the pixel values in region A. P2 corresponds A+B, P3 to A+C and P4 to A+B+C+D. Therefore, the sum over the area D is P4+P1-P2-P3

Figure 3. Some examples of images with license plates in the data set for the experiments.

Boosting for feature selection was first introduced by [26]. In their work, feature selection from a large set of features is performed by Adaboost. The main idea is that each feature corresponds to a single weak classifier and boosting selects an informative subset from these features. Training proceeds similar to the described boosting algorithm. Given a set of possible features F = { f1 ,.... f k } in each iteration step n, the algorithm builds a weak hypothesis based on the weighted training samples. The best one forms the weak hypothesis hnweak which corresponds to the selected feature f n . The weights of the training samples are updated with respect to the error of the chosen hypotheses. Finally, a strong classifier h strong is computed as a weighted linear combination of the weak classifiers, where the weights α n are estimated according to the errors of hnweak as described above. 2) Cascade structure of the boosted classifiers: A cascade of classifiers is a degenerated decision tree where at each stage a classifier is trained to detect almost all objects of interest (license plates in our work) while rejecting a certain fraction of the non-object patterns [28]. For instance, in a face detection experiment, each stage was trained to eliminated 50% of the non-face patterns while falsely eliminating only 0.1% of the frontal face patterns; 20 stages were trained. Boosting can learn a strong classifier from a (large) set of weak classifiers by re-weighting the training samples. Weak classifiers are only need to be better than random guess. The set of weak classifiers are all classifiers which use one feature from our feature pool in combination with a simple binary thresholding decision. At each round of boosting, the feature-based classifier is added that best classifies the weighted training samples. With increasing stage number the number of weak classifiers, which are needed to achieve the desired false alarm rate at the given hit rate, increases (see [28] for a detail). Image representation and features Features usually encode knowledge about the domain, which is not easy to learn from a raw and finite set of input image data. The main purpose of using features instead of raw pixel values as input to a learning algorithm is to reduce the intra-class variability while increasing the extra-class variability and adding insensitivity to certain image variations (e.g illumination). Therefore they make the classification easier. We use two types of features, which are: the Haar-like features [28] and local binary patterns (LBP) [18]. One can think of combining more of such (local) features and also include global features. B.

Figure 4. Examples of positive (license plates) and negative images (without license plates) in the data set for training.

• Haar-like features [28]: These features were introduced by Viola and Jones for face detection and are now widely used in computer vision. We use four different prototypes of features, see Figure 1(a). A two-rectangle feature consists of two regions which have the same size and shape and are horizontally or vertically adjacent. For a three-rectangle feature, the sum for the two outside rectangles is subtracted from the sum in the center rectangle. For the four-rectangle feature the difference between diagonal pairs of rectangles is computed. Finally, for a center-feature the center region is subtracted from the surrounding pixels. These features are calculated at different scales. • A simplified version of LBP [18]: Low-cost LPB features are introduced to effectively describe local features of license plate images. We use a four-neighborhood, i.e. 24=16 patterns, as a 16 bin histogram feature similar to [31]. This is a texture descriptor which captures the statistic of normalized pixel values in a local neighborhood. The LBPvalue of a 3x3 image patch x is calculated as follows (see 3

Figure 1(b)):

LBP ( x) =

∑ s( x

i

− xcenter ) ⋅ 2 i

(5)

i =0

⎧1 z ≥ 0 with s ( z ) = ⎨ ⎩0 z < 0

(6)

The final representation is a histogram of the LBP values obtained by shifting the 3x3 patch in the whole image patch. Note, that the computation of all these feature types can be done very efficiently using integral images [28] and integral histograms [19] as data structures. This allows for exhaustive template matching for the whole image. An integral image, denoted as II, sums up all the pixel values from the upper left up to the current position. More formally, it is defined on an x

image I as

II ( x, y ) =

y

∑∑ I ( x′, y′).

(7)

x′=1 y′=1

The pre-calculation of an integral image for all pixels can be efficiently implemented in one pass over the image. Afterwards, any sum of any rectangular region can be computed by only 4 memory accesses and 3 additions, see Figure 2 for an example. This idea can be easily adapted to represent histograms: for each bin one integral image is built separately. Rotation of the detector can be done by computing the features at different angles for the detection process. Lienhart [15] introduced an additional set of rotated Haar-like features, which are an enriched set of basic features and can be computed efficiently. In [16], Marczack and et.al proposed to use different types of Haar-like features. A previously trained

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classifier is converted to work at any angle, so rotated objects can be detected. A real-time version for the rotational invariant Viola-Jones detector has been reported in [29]. Similar techniques can be implemented in our system. C. Training and detection We build a LPD system based on the boosting learning algorithm and the two kinds of features, Haar-like and LBP, as presented above. We perform training in three scenarios, which will produce three classifiers: one with only Haar-like feature as weak classifier, one with only LBP feature as weak classifier, and one with both Haar-like and LBP features as weak classifiers. After training, the trained classifiers will be applied on images/video for detection and localization of license plates, or will be combined at later stage for improving the performance of the system. After a classifier is trained, it is then applied to a region of interest in an input image (at first the region must be of the same size as used during the training). The classifier outputs a 1 if the region is likely to show the object (license plate), and 0 otherwise. To search for the object in the whole image, one has to apply the trained classifiers exhaustively on the image. The classifier is designed so that it can be easily resized in order to be able to find the objects of interest at different sizes, which is more efficient than resizing the image itself. So, to find an object of an unknown size in the image the scan procedure should be done several times at different scales. Detection is performed by applying the trained classifiers exhaustively on the images. A license plate is considered to be detected if the output confidence value of the classifier is above a threshold (i.e. zero). With a lower threshold, it is more likely an object (LP) is detected, but it is also more likely a false positive (wrongly detected). With a higher threshold, the false positives decreases but some detections could be missed. IV.

EXPERIMENT AND RESULT

In this section we will show the application of the proposed system on real data sets for detection of license plates. We will briefly present the data set, system configuration for experiments and result evaluation. A.

Data set Our work concerns the license plate detection problem, i.e. to specify the location of a license plates (if there is) in an image or video captured by a camera. We aim at building a License plate recognition system that can apply for the situations in Vietnam. We found that there is no public available data set of license plates in such scenarios. So, for conducting experiments, we had to collect data for training and test the proposed framework. This can be considered as our additional contribution as we can provide the data set for researchers for further research and study in future. The data set has been collected from many places in Hanoi city, reflexing real situations of the problem, such as: parking lots, street sides, entry gates. Images are captured at different time in a day, at different lighting conditions (sunshine, shadow, underground park), at different distances (1 to 5 meters) and slightly different viewing angles (up to 15 degrees). This is to ensure that the dataset covers intra- and inter-class variances of object (license plate) and the real working environment. Some pictures are shown in Figure 3.

Figure 5. Some examples of detected license plates.

TABLE I.

PERFORMANCE EVALUATION OF BOOSTED CLASSIFIERS WITH TWO FEATURE TYPES.

Feature types

Recall

Precision

F-measure

Haar-like

84.1

87.3

85.7

LBP

84.6

90.9

87.6

Combine

85.2

96.3

90.4

For training, we captured images that contain at least one license plate (one vehicle) in various background. The data set includes 4000 images. The license plates will be cropped from the images for creating the positive samples data set. The positive sample set contains 1500 image patches of license plates. These license plate patches must cover all text in the plate with a little bit background in the surroundings. The negative samples, i.e. non-license plate, are various nature scenes collected from Internet or taken from the background of the raw images, i.e. ground, building, road, tree. The image size for training is the same with the license plate patterns. In our experiment, the number of the negative examples is more than that of the positive examples to cover variances of background. Some examples of such image patches are shown in Figure 4. All experiments were done on standard PCs Intel Core 2 Duo 2.0 GHz, 2048 Mhz RAM with OpenCV library using C/C++ programming language. We performed two-fold crossvalidation: the whole data set was partitioned into two complementary subsets; training was done on one subset and test (validating) was done on the other subset. The results are presented in the following section. B.

Performance evaluation We show the results of applying our framework for detection of license plates from the data sets. Figure 5 shows the result of LP detection in several images. The images also show backgrounds of real scenes where LPs locate. As one can see, the LPs also appear in slightly different view angles, different contrast and lighting condition. In general, all the LPs with good features have been detected, even almost all difficult ones could also be detected. For some LPs which are partly occluded, they might be detected, might be missed. For some objects that look like LPs, they might be reported as LPs but with low confidence value and have been removed at post processing stage. The system also works well in dealing with different size of LPs. For a quantitative evaluation, we use the common measurement for object detection problem named recallprecision curve (RPC) [3]. This measurement considers the number of false positives with respect to the number of positives in the ground truth and the detected true positives.

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PR =

# TP (8) # TP + # FP

RR =

# TP # TP + # FN

(9)

Fm =

2 ⋅ RR ⋅ PR RR + PR

(10)

(TP–true positives, FP–false positives, FN–false negatives) The precision rate (PR) shows how accurate we are at predicting the positive class. The recall rate (RR) shows how many of the total positive we are able to identify. The Fmeasure (Fm) is the harmonic mean that can be considered as trade-off between recall and precision. We are still working on features and classifiers combination, aim at improving the performance of the system. Due to the lack of public available dataset of license plates for the evaluation, and different methods have been used for evaluation in related works, a fair comparison would be difficult. Although a complete comparison is not met, our experimental results show that in general the performance of our framework is superior in terms of the detection rate and the efficiency. V. CONCLUSION We have presented a new framework for detection of vehicle license plate based on boosting learning technique. Beside the traditional Haar-like feature, which is usually used in boosting frameworks, we employed Local binary pattern for its discriminative power. Moreover, we presented a new mechanism for combining detections at classifier level, which improved the performance of the system. The proposed system has been successfully applied to detection of license plates in images captured from real scenarios. The system can perform real time detection. For future work, we will investigate on feature combination for further improvement of the detection rate, meanwhile reducing processing time. We also plan to develop an OCR module for a complete system for License plate recognition. REFERENCES [1] [2]

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