A Road Trafc Signal Recognition System Based on Template Matching Employing Tree Classier

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A Road Traffic Signal Recognition System based on Template

matching employing Tree classifier

Varun S, Surendra Singh, Sanjeev Kunte R, Sudhaker Samuel R D and Bindu PhilipDepartment of Electronics & Communication

S J College of Engineering

Mysore - 570006, Karnataka, India

Email: [email protected], [email protected]

Abstract

The traffic sign detection and recognition system is an essential module of the driver warning

and assistance system. Smart vehicles are the order of the day. Such vehicles have the ability

to warn drivers of pending situations, remind them of speed limits and even automatically take

evasive action. Due to the visual nature of existing infrastructure, signs and line markings, image

processing will play a large part in these systems. In this paper we present an automated Traffic

sign recognition system allowing an invariance localization to changes in position, scale, rotation,

weather conditions, partial occlusion, and the presence of other objects of the same color. The

reliability demonstrated by the proposed method suggests that this system could be a part of an

integrated driver warning and assistance system based on computer vision technology.

1. Introduction

Traffic signs provide regulatory, warning, or guidance information to drivers. They are designed

to be easily recognized by human drivers mainly because their color and shapes differ from those

present in natural environments. As such, effective and timely conveyance of information from

traffic signs to drivers can definitely decrease the number of traffic accidents. The information

conveyance depends in part on the legibility and recognition of the traffic sign content, which is

a function of traffic sign and observer characteristics. In autonomous vehicles, they should thus

provide the control system with information similar to that offered to human drivers. Traffic Sign

Recognition (TSR) Systems are today being incorporated into the Autonomous Intelligent Vehicles

of the future, which will take crucial decisions regarding speed, trajectory of motion, etc. Such

vehicles will incorporate vision-based vehicle guidance systems that will perform road detection,

obstacle detection and sign recognition.

Some of the early works on traffic sign recognition system are: An automatic target recognition

system developed by Olson and Huttenlocher in [1] uses a hierarchical search tree. Work by Lu et

al. [2] is an example of neural network techniques for traffic sign recognition. Piccioli et al. [3]

used black and white images. After the extraction of the edges, there is a shape analysis looking

for circular and triangular contours. Besserer et al. [4] created a pyramidal structure from the

original image. An edge detector is applied to every image of the pyramid. The edges of an image

joined the edges of the upper image. By analyzing the generated contours, the signs are classified

into triangular, circular, or rectangular signs. De La Escalera et al. in [5] have proposed system

for European warning signs (equilateral triangles with one vertex upward) and circular prohibition

International Conference on Computational Intelligence and Multimedia Applications 2007

0-7695-3050-8/07 $25.00 2007 IEEE

DOI 10.1109/ICCIMA.2007.190

360

International Conference on Computational Intelligence and Multimedia Applications 2007

0-7695-3050-8/07 $25.00 2007 IEEE

DOI 10.1109/ICCIMA.2007.190

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signs. The system has different stages for color segmentation, corner detection, shape recognition,

sign classification. The paper proposes a TSR system developed in two phases: Detection phase and

Recognition phase. In the former, the color thresholding in the RGB color space is used to segment

the image. The features of traffic signs are investigated and used to detect potential objects. The

later phase will be trained to perform the classification and validation. The joint use of classification

and validation modules can reduce the false recognition rates.

The rest of the paper is organized as follows. Section 2 gives a brief overview of the proposed

TSR system. Different modules used in TSR system are described in detail in section 3. Experi-

mental results and conclusion are discussed in section 4 and 5.

2. Overview of the TSR system

After acquiring image of the object through a camera, the image is examined to determine

whether a traffic sign is present in it or not in the first phase. In the next phase, the detected traffic

sign is matched with three templates corresponding to circle, circle-bar and triangular respectively.

This is based on the fact that all the mandatory and cautionary signs used in India are characterized

by equilateral triangles or circular shapes with or without a cross. These three templates form our

basic templates. The remaining traffic signs are derived from these templates. Hence they are re-

ferred as derived templates. For example, all the speed signs are derived templates which belong to

the circular basic template. Therefore the basic template category of the traffic sign is determined.

In the last phase, the detected traffic sign is matched against the derived templates belonging to the

same basic template class.

Figure 1. Three-phase decision tree used for traffic sign recognition.

3. Modules of TSR system

The TSR system gives an accurate output to the user through the interaction of five modules

namely, (a) Pictures module (b) Objects module (c) Database module (d) Classifier module (e) GUI

module. The interaction between these modules is illustrated in Figure 2.

3.1. Picture module

This module deals with the following: (a) Image Capturing (b) Preprocessing the image (c)

Segmentation of the image on the basis of color information (d) Retrieval of all the objects present

in the current image.

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Figure 2. Interaction between the various modules of the TSR system.

In this module, the image is first preprocessed to remove the noise. The R, G and B values for

each pixel are then determined. The sum of green and blue pixel components is then compared

with 1.5 times the red pixel component for each pixel. Pixels corresponding to relatively higher red

component values constitute the feature pixels and are an indication of the objects of interest. A

binary segmented image is then created using the known coordinates of the feature pixels. Figures

3(a) and Figure 3(b) show the original image and its segmented version respectively.

Figure 3. Example of a binary segmented image (a)Original Image (b)Segmented

Image.

3.2. Objects module

This module deals with the following: (a) Detection of the various objects present in the image

(b) Determines whether the objects are valid traffic signs or not (c) Determines the basic template

class to which the detected traffic sign belongs to.

In this module, all the 8-pixel connected objects are considered and labeled with a unique tag.

If the number of detected objects is greater than 50, then it is an indication to the presence of

unnecessary objects. Such objects are removed through a combination of morphological opening

and closing processes [6].

For each of the detected objects, the following tasks are performed:

The object is first enclosed in a virtual quadrilateral.

This quadrilateral is then examined to determine whether it is an approximate square with

40X40 or greater dimensions. These conditions are the preliminary requirements that need

to be satisfied to enable efficient extraction of features.

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normalization, the computed values are binned into ten containers. The divergence of the detected

Traffic Sign is computed with respect to the previously computed normalized city block distances

corresponding to the basic template it has been categorized under. If the divergence is below a

certain threshold, the detected traffic sign is considered valid. Table 1 illustrates the previously

computed binned city block distances corresponding to the three basic templates, namely circle-bar

(CB), circle(C) and triangle (T).Table 1. Normalized City Block Distances Corresponding to the Basic Templates

The holes in the feature image are then filled thereby creating a temporary mask. The mask

obtained is used along with the original feature image to modify the segment of the original image

containing the traffic sign such that the traffic sign features are superimposed on a white back-

ground. This is called the Data Image. This is accomplished as follows: 1. The pixels in the

original image segment, corresponding to non-zero mask elements, are retained. The remaining

pixel components are set to 1. 2. The coordinates of all the pixel components, corresponding to

the non-zero pixels in the binary feature image, are determined and the corresponding pixels in the

image segment are set to 1.

The Data image is then inverted such that the features correspond to the non-zero pixels. If the

number of non-zero pixel components is greater than a suitable threshold indicating presence of

sufficient feature data, the final classification step is carried out. Forward matching and reverse

matching is performed by correlating the inverted data image against the derived templates. Thederived template corresponding to the minimum obtained score is determined and a suitable media

output is provided to the driver.

3.4. Database module

The template database is built which will be used to determine the information conveyed by the

traffic signs. Upon construction, it takes the added functionality of inserting and retrieving records

from the database as and when the TSR system dictates. The traffic sign templates, endorsed by the

Delhi Traffic Police, are frequently used on national & state highways. These templates are used

for compiling the template database.

3.5. GUI module

This module deals with

Interaction with the various modules of the TSR system

Accepts the input images from a suitable image capturing device such as a camera

Provision of an audio output to the user providing the driver with information that has been

extracted from traffic signs.

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4. Experimental results

A test set was created by capturing about 2000 images of traffic signs consisting of triangle

shaped, circular shaped with or without a cross. The traffic sign recognition algorithm gave excel-

lent results and met the constraints of real-time situations. The recognition rates for different basicshapes obtained are illustrated in Figure 6. The algorithm could detect the signs rotated up to 200.

Also the system was tested with signs which are partially occluded and got recognition rate about

82%.

5. Conclusion

In this paper we propose a Traffic Sign Recognition system which is capable of recognizing

the traffic signs at high accuracy. The system is invariant to rotation, partial occlusion of traffic

signs, and the presence of other objects of the same color in the image. By further extending

the proposed work it can be used in an autonomous vehicle guidance system which make crucial

decisions regarding speed, trajectory of motion and has a myriad of applications.

Figure 5. System Performance.

References

[1] C. Olson and D. Huttenlocher, Automatic target recognition by matching oriented edge pixels, IEEETransactions on Image Processing, Vol. 6, No. 1, 1997, 103113.

[2] S. Lu and A. Szeto, Hierarchical Artificial Neural Networks for Edge Enhancement, Pattern Recog-

nition, Vol. 26, No. 8, pp. 1993, 427435.

[3] Piccioli, E. De Micheli, P. Parodi, M. Campani, A Robust Method for Road Sign Detection and

Recognition, ECCV, 1994, 495500.

[4] B. Bessere, S. Estable, B. Ulmer, and D. Reichardt, Shape classification for traffic sign recognition,

In Proc. 1st IFAC Int. Workshop Intelligent Autonomous Vehicles, 1993, 487492.

[5] Arturo de la Escalera, Luis E. Moreno and Miguel Angel Salichs, Road Traffic Sign Detection and

Classification, IEEE Trans. on Industrial Electronics, Vol. 44, No. 6, 1997, 848859.

[6] Anil K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, 2001.

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A Road Trafc Signal Recognition System Based on Template Matching Employing Tree Classier