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Robot Soccer Vision: An Overview for New Learner

Najmi Hafizi Bin Zabawi1, Khairuddin Omar

2

1Electrical Engineering Department, Politeknik Sultan Haji Ahmad Shah, Pahang, Malaysia

2Faculty of Information Science And Technology, National University Of Malaysia, Malaysia

[email protected]

[email protected]

Abstract Vision is a part of a robot soccer system. It completes

the system by providing the eye to see what occurs inside the

pitch during the game. Similarly as in humans, the sight is then

sent to the brain. The control system needs to be processed

before making any decision to be sent back to robots on the field

to react. The process will be repeated over and over again. This

paper discusses the robot soccer vision system which deals with

surface theory and some examples of applications used.

Suggestions for future improvement are included at the end ofthis paper.

Keywords image calibration, image processing, vision system,

image filter, object recognition

I. INTRODUCTIONOne of the main elements in robot soccer is the vision

system. Robot soccer uses a visual imaging method to track

objects in order to play the soccer game. In the vision system,

the components involved are colour calibration, image

processing and object recognition.Colour calibration is about setting up the camera and colour

elements to perform the best image capturing quality to feedto the image processing stage. Image processing is the task

that is carried out by the robot soccer system to process the

images that are obtained from a CCD camera. A CCD (Charge

Coupled Device) is chosen for robot soccer as it is stable,portable and accurate besides other advantages [1].

Object recognition is the next stage performed on what has

been yielded by the image processing task. During the image

processing stage, the image is filtered using algorithms and

the result is channelled as an output to the object recognition

process in the decision-making stage.

Fig. 1 Role of the vision system in the robot soccer system as carried out

by the overhead camera [2]

II. CURRENT DEVELOPMENTA. Colour calibration

Colour calibration is important as a robot soccer game is

most dependent on the colour recognition application in

determining a robots team members. Every robot is mounted

with a colour tag designed to distinguish each of them by wayof their roles; i.e. those of goalkeeper, striker and defender.

The colour tag may be designed in various formats such as

quadrilateral, oblique and round colour patches [3][4][16].

Although they may be different in design, they have one thing

in common - the colour of patches used. Every colour tag

must consist of that teams identifying colour such as blue or

yellow. This is to avoid confusion and tracking mistakes to

each teams vision system. In this case, colour space plays the

important role in this colour determination task. Colour space

comes from different colour models such as RGB, YUV and

HSV. Researchers commonly use these colour modelsaccording to their research requirement. Some of them prefer

to use RGB, some YUV and the rest HSV. All these modelsare used by the computer to process images, whereby each of

them has their own advantages as compared to each other.

B.Image processingImage processing is the step where images captured by the

video camera are processed by computer. The objective is to

enable the vision system to recognize target objects such as

the ball and robots on the game field. In this step, the image

frames usually undergo several filtering processes todistinguish between desired and undesired object images. The

common image filtering process terms include Rao-

Blackwellised particle filters [5][6], Kalman filter [7][8],Gaussian filter, Sobel filter [9], Laplacian [10], morphological

filters [11], Bayesian [12], Monte Carlo filter [13], and Canny[14].

C. Object recognitionObject recognition refers to the task of the vision system in

identifying the object which has been obtained from theprevious filtering task. The techniques that the system uses in

object recognition process include colour based identification

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978-1-61284-406-0/11/$26.00 2011 IEEE

IR-06

2011 International Conference on Pattern Analysis and Intelligent Robotics

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[10][15], environment based [10], behaviour based [17] and

pattern based recognition [18].

III.GENERAL ARCHITECTUREA. Colour models

Colour calibration depends much on colour space analysis.

Colour spaces that are popular in robot soccer games comefrom different colour models such as RGB, YUV and HSV.

The RGB (red, green, blue) model consists of three basic

colours which exist in the natural world. The RGB colour

format is usually used by a computers graphic card to capture

images from a camera but the biggest disadvantage of this

model is that it is not suitable to the human eye [19]. This is

because the values of the components R, G and B are highly

inter-related and cannot be understood by bare eyes.

Furthermore, the components are very sensitive to

environmental conditions such as illumination and light

intensity which will affect the stability of the components

values [8]. That is why researchers tend to use other colour

models in performing their research.

The YUV colour model on the other hand comes from the

RGB colour model, in which component Y represents

brightness or luminosity, while the colour information is

represented by U for hue and V for saturation [20][21][22].

HSV (hue, saturation, value) is the most popular colour

model used by programmers to represent the colour data in

computers as the components are more familiar to humans

than the previous colour model components [23]. Most of

RGB data that is collected from the camera is converted first

into the HSV format before other processes are executed [24].

Hue has a magnitude such that HSV colour space is

sometimes called as HSI where I represent intensity

replacing the V for the value component.

Fig. 2 HSV colour space model [19]

The HSV model can be represented as an inverted cone as

shown in Fig. 2 above. In the cone, the 360 circle denotes hue,

radius from 0 to 1 denotes saturation; purity being the most at

0 and least at 1, and the cones vertical axis represents thedark and the bright sides of the model. Hue is a property that

relates to the dominant wavelength in the light spectrum [26].

Hue represents the type of colour that a human can understand.

For example Fig. 3 shows two colour patches of robot soccer

jerseys which use yellow and red respectively. The left image

shows the jersey with the red almost similar to the right one,but looking at their HSV values, the left image has a higher

Hue value compared to the right one, while its Saturation

value is lower. However, both patches preserve the same

value for V or the Value component.

Fig. 3 Robot soccer patch sample colour comparison;Left (Reds HSV : 238, 205, 125)

Right (Reds HSV : 2, 232, 125)

B.Image filtersAmong the purposes of image processing practice are

cleaning noise from the concerned image, improving contrastbetween neighbouring regions or features, applying smoothingor elimination of features at certain scales and keeping only

features at a wanted scale [27]. A filter is a program dedicated

to process the raw information as the input and the

information of interest as the output [28]. In short, the main

purpose of a filter is to purify an image of the unwanted noise

that may affect the scene originality of the image. There aremany image filtering techniques available and they are

extensively used by scholars to perform their research. Among

the filters is the Bayes filter which refers to a method of

applying prediction cycle to forecast the state of a method of

using a time-dependent system from sensor measurement.

Two types of Bayes filters are the Kalman filter and theparticle filter [29].

A Kalman filter is an algorithm of data processing that

performs recursively and which predicts the condition of a

noisy linear dynamic system. The filter was found by Richard

Kalman in 1960 and some believe that the discovery is one ofthe greatest discoveries in statistical estimation theory in the

twentieth century [30][31]. This most widely used filter is

basically chosen by many researchers for its ability to predict

the future state of a dynamic system variable which is difficult

for people to control [30]. In image processing practice, the

Kalman filter works efficiently with image data that isrepresented by the Gaussian function a function that is used

to reduce image noise and detail [30]. However, forinformation that contains non-linear orientation data, the

Kalman filter needs to be modified to cater for the non-

linearity attributes, which becomes known as the Extended

Kalman filter (EKF). Even so, the EKF still has two

significant drawbacks, which are implementation difficulty

and instability of the filter. This situation has led to the

invention of the Unscented Kalman filter (UKF) [31].

The Particle filter basically is a Bayesian based filter that

processes a sequence of action and perception as the input and

outputs the belief distribution of the subject that acts and

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perceives [28]. It works by representing the required posterior

probability density function by a set of random samples withassociated weights and to compute estimates based on these

samples and weights [32].

Fig. 4 Comparison of algorithms fornonlinear image filtering [33]

The Morphological filter is an evolution of mathematical

morphology which focuses on identifying the geometrical

structure of image objects. One of the advantages of using amorphological filter in image processing is it can solve a non-

linear task such as a non-Gaussian noise function [27]. It

smooth out object outlines by filling small holes which

eliminates small projections [25].

Another popular image processing technique is the edgedetection method. Edge detection is a process of extracting

edges from an image. Researchers have found that the most

important data lies in its edges. The edges will form the object

boundaries on an image. Edges are identified as the changes

of gray value from a pixel to the next pixel of an image. By

only focusing on edges, other redundant pixel information onan image can be ignored and this will save time while

preserving the significant structural attributes of the image

[34]. There are several methods of performing edge detection,

with the majority of them being able to be categorized into

two groups, which are gradient-based and Laplacian based.

Under the gradient based group, Sobel, Canny, Roberts and

Prewitt are the examples [35] while under the Laplacian based

group, Marr-Hildreth, Laplacian of Gaussian etc. are placed.

The first group focuses on finding the minimum and

maximum values on an image to detect edges, during the first

derivative of the image. The Laplacian group on other hand

detects edges by finding zero crossings in an image during the

second derivative of the image [36]. The derivatives arerepresented by some equation algorithms that are not

discussed in this paper. Fig. 5 demonstrates the examples that

result from applying the two edge detection methods that are

discussed above.

Fig. 5 Left: Input image, Middle: Gradient method,Right: Laplacian method [36]

(a) (b)

(c) (d)

Fig. 6 Significance of morphological filter on

noisy image [25]

Some researchers apply both morphological and edge

detection methods to filter out their images. As demonstrated

by Mohamed Roushdy (2006) in his journal, the noisy image

that is shown in Fig. 6 (a) will be as in (c) after the Sobel filter

is applied while on the other hand it will be as in (d) if the

image is applied with a morphological filter first as shown in(b) [25].

The Sobel filter is used to perform the edge detection

process. This method works by assuming that an edge happens

when there is a sudden intensity gradient in the image [34]. In

the Sobel filtering procedure, the first step is to measure the

edge tendency value of each pixel in vertical and horizontal

axes. With a threshold value being applied, the pixel that is

above the threshold value will be set to an edge value. Then

the process will be repeated for the rest of the unset pixels

until no pixels are left unchecked [36].

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C. Object recognitionColour-based identification techniques i

various colour patch designs such as quadri

round colour patches [3][4][16]. The colour

sometimes used in colour based recogniti

example, the HSV colour space has bee

Honge et al. to perform object recognition

segmentation combined with the edge dete

while YUV colour space is preferred by

and Bin Ge for the same purpose [38]. Faal. proved that YUV colour space is better

colour space in terms of object recognition

object is exposed to different light conditioThe Hough transform is a method whic

The Hough transform (HT) is a technique

to separate features of a particular shape in

an algorithm that is able to solve proble

line detection and definition. It was introdu

in 1962 and patented by IBM [40]. Originwas in particle physics, in which it was us

lines and arcs in the pictures captured in cl

Basically, Hough transform is used as pre

before an image is further processed

transform algorithm is best if used to detect

Hui Zhang et al. has proven its ability in the

a robot soccer ball even without botheri

colour. They used the Sobel filter first to

and the direction of gradient before ap

transform algorithm [9]. Besides the colo

the combination of neural network and fuzcan also be applied in this recognition task [

Fig. 6 below is the summary chart of al

methods that have been discussed above.placed according to its type and group wi

much on their specific function and chara

give a new learner an overview of the techthe image processing field that are related

world.

Fig. 6 Placement of the mentioned image filtering a

processing field of study

Image Filtering Methods

Bayesian

Linear /Gaussian

Kalman Filter(KF)

Non-linear

UnscentedKF, Extended

KFParticle Filter

Monte CarloRao

BlackwellisedMarkov

Morphology

HoughTransform

(for linedetection)

Laplacian-based

Marr-Hildreth,

Laplacian ofGaussian

ncludes the use of

ateral, oblique and

model approach is

on tasks [15]. For

n chosen by Ren

work using colour

tion technique [23]

Genichi Yasuda

nny F. L. Tong etcompared to RGB

when the targeted

s [22].can also be used.

which can be used

an image [39]. It is

s associated with

ced by Paul Hough

lly, its applicationd for detecting the

ud chambers [41].

processing method

[39]. The Hough

a curve or a circle.

ir research to track

ng about the ball

detect edge points

lying the Hough

r space technique,

y logic techniques42].

l filtering types or

Each technique isthout emphasizing

teristic; enough to

niques available into the robot soccer

proaches in the image

IV.FUTURETo improve the performance

soccer, several things can be d

recognition technique in identi

the pitch. The robots are no lo

use face pictures instead.

developed using OpenCV whi

Haar-like features and it has p

human faces anda comical face

The advantage of this trackin

filtering process that is comm

image can be avoided since t

colour images, but also blackfuture we can implement a ne

longer uses colour patches, but

perform robot localization.

Fig. 7 Face tracking program trac

track a real face image, but the comicalike pictures can be mounted on socce

patch j

Secondly, the ability of thdifferent light conditions need

that the vision system shouldpitch, robots and the ball wit

intervention. This can be realiz

a neural network algorithm th

learn and identify the desirregardless how the light condit

process is not only subjected

game, but can be based on all t

larger scale. The recognition

stored to be used in the next g

EdgeDetection

Gradient-based

Sobel,Roberts,Prewitt

Canny (forgray scale)

DIRECTION

in the vision system of robotne. First, we can use the face

ying and locating the robot on

nger using colour patches, but

sample program has been

h is able to track faces using

roven its ability to track both

as well as shown in Fig. 7 [43].

method is that the complex

only carried out on a colour

is method can not only track

and white ones. Hence, in thekind of robot jersey that no

only face similar pictures to

ks face-like features. Not only can it

l hand- drawn face as well. The face-r robots, replacing the normal colour

rseys.

e system to track objects into be enhanced. This means

be able to recognize the fieldout much human calibration

d by applying fuzzy logic and

t enable the vision system to

ed objects time after time,ions are. It means the learning

to being confined within one

he games it experiences in the

ata of the objects should be

me and eventually the system

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Learning, IEEE International Conference on Industrial Technology,

2003.[37] LaViola, J.J., Jr.; , "A comparison of unscented and extended Kalman

filtering for estimating quaternion motion," American Control

Conference, 2003. Proceedings of the 2003, vol.3, no., pp. 2435- 2440

vol.3, 4-6 June 2003.[38] Bin Ge; Gen'ichi Yasuda; Fuliang Yin; Hongwei Zhao; , "Object

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World Congress on , vol.2, no., pp.10245-10250, 0-0 0.

[39] Brahim Nini , Brahim Mehelain , Bilel Flifel . Agent-based HoughTransform: A Way to the Improvement of the Execution Time in the

Detection of the Dominant Straight Line in an Image. International

Journal of Computer Applications (0975 8887)Volume 11 No.2,

December 2010.

[40] Kyewook Lee, Application Of The Hough Transform, University of

Massachusetts, Lowell. January 2006.[41] Danko Antolovic, Review of the Hough Transform Method, With an

Implementation of the Fast Hough Variant for Line Detection

[42] Hung-Ching Lu; Cheng-Hung Tsai; , "Image Recognition Study via theNeural Fuzzy System," Intelligent Engineering Systems, 2006. INES

'06. Proceedings. International Conference on , vol., no., pp.222-226,0-0 0.

[43] Bradski, G. & Kaehler A. (2008). Learning OpenCV Computer

Vision with OpenCV Library. O Reilly.

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