A FUZZY SYSTEM FOR INTEREST VISUAL DETECTION BASED

ON SUPPORT VECTOR MACHINE

Eugenio Aguirre, Miguel Garc

ıa-Silvente, Rui Pa

Department of Computer Science and A.I., E.T.S. Ingenier

ıa Inform

atica

University of Granada, 18071 Granada, Spain

Rafael Mu

noz-Salinas

Deparment of Computing and Numerical Analysis, E.P.S.

University of Cordoba, Cordoba, Spain

Keywords:

Human-Robot Interaction, Interest Detection, Head Pose Estimation, Fuzzy Logic, Support Vector Machine.

Abstract:

Despite of the advances achieved in the past years in order to design more natural interfaces between intelligent

systems and humans, there is still a great effort to be done. Considering a robot as an intelligent system,

determining the interest of the surrounding people in interacting with it is an interesting ability to achieve.

That information can be used to establish a more natural communication with humans as well as to design

more sophisticated policies for resource assignment. This paper proposes a fuzzy system that establishes a

level of possibility about the degree of interest that people around the robot have in interacting with it. First, a

method to detect and track persons using stereo vision is brieﬂy explained. Once the visible people is spotted,

their interest in interacting with the robot is computed by analyzing its position and its level of attention

towards the robot. These pieces of information are combined using fuzzy logic. The level of attention of a

person is calculated by analyzing th e pose of his head that is estimated in real-time by a view based approach

using Support Vector Machines (SVM). Although the proposed system is based only on visual information, its

modularity and the use of fuzzy logic make it easier to incorporate in the future other sources of information

to estimate with higher precision the interest of people. At the end of the paper, some experiments are shown

that validate the proposal and future work is addressed.

1 INTRODUCTION

The interaction between Intelligent Systems and hu-

man beings is a topic that is focusing a great research

effort nowadays. The development of natural and

multimodal interfaces is needed to enhance the inter-

action abilities of current Intelligent Systems. In par-

ticular, within the area of Robotics, the development

of successful robotic systems applied to service tasks

in home and ofﬁce environments implies the genera-

tion of natural human-robot interfaces. In that sense,

important issues that must be taken into account are

how robots can detect the presence of persons around

them and how do they recognize when and how long

a person is interested in establishing an interaction.

In order to achieve this goal, it is necessary to solve

several problems. First, a robot must be able to de-

tect persons in its vicinity and track their movements

over time. People tracking is not an easy task since

several persons could be moving at the same time,

crossing their trajectories and occluding each others.

We can ﬁnd many works in the literature on this topic

(Fritsch et al., 2003; Snidaro et al., 2005). The tech-

niques to perform the detection and tracking are fre-

quently based on the integration of different informa-

tion sources such as: skin color, face detectors, visual

analysis of the motion or laser range ﬁnder.

Once a robot is able to recognize and track the

persons in its vicinity, it should be able to detect their

interest in establishing an interaction with it. In that

task, several types of signals from the human can

be taken into account (both verbal and non-verbal).

Some authors (Bennewitz et al., 2005) use sound

source localization or speech recognition besides vi-

sual perception to detect which persons are the most

interested. In other cases, facial expressions (Song

181

Aguirre E., García-Silvente M., Paúl R. and Muñoz-Salinas R. (2007).

A FUZZY SYSTEM FOR INTEREST VISUAL DETECTION BASED ON SUPPORT VECTOR MACHINE.

In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, pages 181-188

DOI: 10.5220/0001632601810188

 SciTePress

et al., 2001) or hand gestures (Ghidary et al., 2002)

are analyzed. Finally, other authors (Kulic and Croft,

2003) propose the use of non-verbal signals present

in physiological monitoring systems that include skin

conductance, heart rate, pupil dilation and brain and

muscle neural activity.

In regards to the role of the fuzzy logic in robotics,

an extensive catalogue of the uses of fuzzy logic in

autonomous robots can be found in (Safﬁotti, 1997).

Fuzzy logic has been successfully applied to a multi-

tude of problems such as: design of controlling be-

haviors for navigation, behavior coordination, map

building, integration of deliberative and reactive lay-

ers, etc (Aguirre and Gonz

alez, 2000). Lately, fuzzy

logic has also been applied to the area of human-

robot interaction. In (Bien and Song, 2003) sev-

eral soft computing techniques are applied to service

robotic systems for comfortable interaction and safe

operation. Fuzzy logic is used for recognizing facial

emotional expression and for coordinating bio-signals

with robotic motions. In (Kulic and Croft, 2003) sev-

eral sets of fuzzy rules are used for estimating intent

based on physiological signals.

In this work we are interested in computing a

value of possibility of the interest of a person to inter-

act with the robot. This value is computed using only

visual information, but the modularity of the system

makes easy the posterior incorporation of other types

of input data as sound or laser range ﬁnder. The inter-

est is computed according to the position of the person

and its degree of attention. In a ﬁrst step, people de-

tection and tracking problems are solved by a stereo-

scopic system. The use of stereo vision brings sev-

eral advantages when developing human-robot appli-

cations. On the one hand, the information regarding

disparities becomes more invariable to illumination

changes than the images provided by a single camera,

being a very advantageous factor for the background

estimation (Darrell et al., 2001). Furthermore, the

possibility to know the distance to the person could

be of great assistance for the tracking as well as for a

better analysis of their gestures.

Once the surrounding people is spotted, we pro-

pose a new method for estimating the interest of the

detected people in interacting with the robot by means

of computer vision and fuzzy logic. The person’s at-

tention is detected by the analysis of the pose of his

head. To detect the head pose we have employed a

view based approach using Support Vector Machines

(SVM) (Cristianini and Shawe-Taylor, 2000) that let

us classify the head pose in real time.

The approach presented in this work is not only

valid for robotic applications. It can also be employed

in intelligent systems that use stereoscopic devices.

For example, it can be applied in intelligent spaces

where one or several intelligent devices wish to inter-

act with people according the interest shown by each

person.

The remainder of this paper is structured as fol-

lows. Section 2 gives an general overview of the

hardware and software system, describing the method

employed for the detection and tracking of people in

the surroundings of the robot. In Section 3 it is ex-

plained the SVM based approach to estimate the head

pose and the fuzzy system for estimating the interest

of people. In Section 4 it is shown the experimenta-

tion carried out, and ﬁnally, Section 5 outlines some

conclusions and future works.

2 PEOPLE DETECTION AND

TRACKING

The hardware system is comprised by a laptop to pro-

cess the information, a stereoscopic system with a

binocular camera (PtGrey, 2005) and a Nomad 200

mobile robot (see Fig. 1).

Figure 1: Robot with stereo vision system.

The ability of detecting and tracking people is fun-

damental in robotic systems when it is desirable to

achieve a natural human-robot interaction. They are

achieved in our architecture by combining stereo vi-

sion and color using plan-view maps. Following, the

process for people detection and tracking is explained

in a summarized way. The readers more interested

in this process are referred to (Mu

noz-Salinas et al.,

2006).

Our robot has a stereo camera that is mounted

on a pan-tilt unit (PTU). The stereo camera captures

two images from slightly different positions (cali-

brated stereo pair) that are transferred to the com-

puter to calculate a disparity image containing the

ICINCO 2007 - International Conference on Informatics in Control, Automation and Robotics

182

points matched in both images. Knowing the intrin-

sic parameters of the stereo camera it is possible to

reconstruct the three-dimensional position p

cam

of a

matched pixel (u, v). Then, the points captured are

translated to a “robot” reference system, placed at

the center of the robot at ground level in the direc-

tion of the heading of the robot. Generally, the num-

ber of points captured by our stereo system is very

high. In order to perform a reduction of the amount

of information, the points captured by the camera are

orthogonally projected into a 2D plan-view map O

named occupancy map (Harville, 2004; Haritaoglu

et al., 2002; Hayashi et al., 2004).

The next step in our processing, is to identify the

different objects present in O that could correspond to

human beings (human-like objects). For that purpose,

O is processed with a closing operator in order to link

possible discontinuities in the objects caused by the

errors in the stereo calculation. Then, objects are de-

tected as groups of connected cells. Those objects

whose area are similar to the area of a human being

and whose sum of cells (occupancy level of the ob-

ject) is above a threshold θ

occ

are considered human-

like objects. This test is performed in a ﬂexible way

so that it is possible to deal with the stereo errors and

partial occlusions. However, the human-like objects

detected might not belong to real people but to ele-

ments of the environment. The approach employed

in this work to detect a person consists in detecting if

any of the human-like objects found in O show a face

in the camera image.

Face detection is a process that can be time con-

suming if applied on the entire image, thus, it is only

applied on regions of the camera image where the

head of each object should be (head region). As the

human head has a typical average width and height,

the system analyzes ﬁrst if the upper part of a human-

like object has similar dimensions. If the object does

not pass this test, the face detector is not applied to

it. This test is performed in a ﬂexible manner so

that it can handle stereo errors and people with dif-

ferent morphological characteristics can pass it. If the

human-like object passes this test, the corresponding

region in the image is analyzed to detect if it con-

tains a face. The face detector employed is based on

the face detector of Viola and Jones (Viola and Jones,

2001) which was later improved by Lienhart (Lienhart

and Maydt, 2002). We have employed the OpenCv’s

Library (Intel, 2005) implementation that is trained to

detect frontal human faces and works on gray level

images.

Once a face has been detected on a human-like

object, a color model of the person torso is created

(Comaniciu et al., 2000). The idea is to assist the

tracking process by capturing information about the

color of the clothes of the user so that the robot can

distinguish him/her from other people in the environ-

ment. Therefore, pixels around what it should be the

chest of the person are used. The position of the chest

in the camera image is estimated as 40 cm below the

top of the head region. The size of the region used to

create the color model depends on the distance of the

person from the camera. When the object is far from

the camera the region used is smaller to avoid includ-

ing pixels from the background and it becomes bigger

when the object is near to the camera.

Tracking consists in detecting in subsequent

frames the human-like object that corresponds to the

person being tracked. The Kuhn’s well-known Hun-

garian Method for solving optimal assignment prob-

lems (Kuhn, 1955) is employed for that purpose.

Two pieces of information are combined (position and

color) to assign a value to each human-like object in-

dicating its likelihood to be the person being tracked.

On one hand, a prediction of the future position of the

person being tracked is calculated using the Kalman

ﬁlter. The nearer a human-like object is from the

position estimated for the person being tracked, the

higher likelihood it will have to be him/her. On the

other hand, color information is employed to achieve

a more robust tracking. The more similar the color

of a human-like object is to the clothes’ color of the

person being tracked, the higher likelihood it will

have to be him/her. Both likelihood are combined

so that when the person being tracked is near oth-

ers, color information can help to distinguish him/her.

The human-like object with highest likelihood is con-

sidered to be the person being tracked if its likelihood

value exceeds a certain threshold. In that case, the

Kalman ﬁlter is updated with the new observations

and also the color model of the person is updated so

that it can adapt to the illumination changes that take

place.

When the position of the person being tracked

is located, the system determines the location of his

head in the camera image. In this work, the head is

modeled as an ellipse whose size in the camera image

is determined according to the distance of the person

to the camera. Firstly, the system calculates an initial

estimation of the head position in the camera image

based on stereo information. Then, the initial position

is reﬁned by a local search process. For that purpose,

the gradient around the ellipse perimeter is examined

in order to determine the likelihood of a position using

the Birchﬁeld’s method (Birchﬁeld, 1998). The posi-

tion with higher likelihood is considered the person’s

head position.

A FUZZY SYSTEM FOR INTEREST VISUAL DETECTION BASED ON SUPPORT VECTOR MACHINE

183

VL L M H VH

0 1

Interest

Left Center

0 deg 90 deg 180 deg

Right

Angle

Low Medium High

0m 1.5m 2.5m 8m

Distance

Low HighMedium

0 1

Attention

(a) (b) (c) (d)

Figure 2: Fuzzy sets of the linguistic variables: (a) Distance (b) Angle (c) Attention (d) Interest.

3 INTEREST DETECTION

In the previous section, we have described as the robot

is able to detect and track them the persons in its

vicinity by using the stereo system. This section ex-

plains our approach for estimating the interest of the

detected people in interacting with the robot by means

of fuzzy logic. The approach presented in this work

is based on stereo vision but the system can be eas-

ily expanded to merge other sources of information.

The advantages of using fuzzy logic are mainly three.

Firstly, the robot has to deal with information from

the stereo system that is affected by uncertainty and

vagueness. Fuzzy logic is a good tool to manage

these factors using linguistic variables. Secondly, the

human knowledge can be usually expressed as rules.

Fuzzy logic allows to establish relationships among

the variables of a problem through fuzzy rules pro-

viding an inference mechanism. Finally, there are

methods in fuzzy logic to fuse the results from several

fuzzy rules in order to achieve a ﬁnal overall result.

Therefore, the system designed in this work, based

exclusively in stereo information, can be integrated

with other fuzzy systems using other types of infor-

mation as source sound localization, gesture analysis

or speech recognition systems.

In this work, the determination of the degree of

interest of a person is based on its position and its de-

gree of attention. The position of a person is analyzed

using both its distance to the center of the robot and its

angle in respect to the heading direction of the robot.

The ﬁrst feature is measured by the linguistic variable

Distance and the second one by the linguistic variable

Angle. These linguistic variables have three possible

values each of them, that are shown in Fig. 2. The

meaning of these two variables is the following: if the

person is detected near to the robot and more or less

centered with respect to it, then we consider that the

person is more interested in establishing interaction

with the robot than when the person is far or at the left

or right side of the robot. Nevertheless, the position

of the person is not enough to determine his interest

in interacting with the robot. Thus, the third feature

shown in this paper is the person’s attention detected

by the analysis of the pose of his head. To detect the

head pose we have employed a view based approach

using SVM that is explained in the next section.

3.1 Estimating Face Attention using

SVM

One of the most prominent cues to detect if a person is

paying attention to the system is the orientation of his

face, i.e., a higher degree of attention can be assumed

when a person is looking at the system than when it

is backwards. This section describes our approach for

face attention estimation.

We have divided head poses in three main cat-

egories: “A” that comprehends all the frontal faces

(faces looking directly at the camera), “B” that com-

prehends all the slightly sided faces (faces looking to

some point slightly above, below or aside from the

camera) and “C” that comprehends all the other faces

(side faces, faces looking at some point in the ceiling

or ground, backward heads). Figure 3 shows exam-

ples of each one of the categories employed.

Figure 3: Head Pose Estimation: Classes A, B and C.

We have created a head pose database comprised

by a total of 4000 samples equally distributed among

the three classes. The database contain images of 21

different people (men and women), of different races,

with different hair cuts and some of them wearing

glasses. The database samples were manually clas-

siﬁed into categories “A”, “B” or “C” according to

where people were looking at. All the images are

ICINCO 2007 - International Conference on Informatics in Control, Automation and Robotics

184

gray-scale and 48x40 sized.

Since the information contained in the patterns

is redundant, we have applied Principal Component

Analysis (PCA) to reduce the data dimensionality.

PCA (Henry and Dunteman, 1989) is a technique

widely employed for dimensionality reduction able to

retain those characteristics of the data set that con-

tribute most to its variance, by keeping lower-order

principal components and ignoring higher-order ones.

Such low-order components often contain the “most

important” aspects of the data. The more high-order

characteristics we remove, the faster the process of

training and estimation. However, it is important

to use a number of characteristics that allow us to

achieve good results without affecting our need to

have real time results. We made some tests with dif-

ferent number of characteristics and we determined

that 50 characteristics allowed a good trade-off be-

tween classiﬁcation accuracy and computing time.

The training process has been carried out using

SVM. For that purpose, we have employed the libsvm

library (free software available in Internet (Chang and

Lin, 2006)). For more information about SVM, the

interest reader is referred to (Cristianini and Shawe-

Taylor, 2000). To certiﬁcate that results were satisfac-

tory before applying the model we trained the SVM

with 85% of the data set and kept the remainder 15%

to test the model generated. The result on the test set

was of 93.14% of accuracy.

Our system estimates the attention of each de-

tected person in real time despite of his/her move-

ments. For that purpose, the head location in the cam-

era image of each person is determined using the tech-

niques described in Sect. 2. The head region of each

person is resized to 48x40 pixels and then, the ﬁrst 50

principal components are extracted and passed to the

SVM classiﬁer.

SVM estimates the head pose in one of the three

categories previously indicated. However, the classi-

ﬁer output is an instantaneous value that does not take

into account past observations. In order to consider

past observations, we deﬁne the variable HP

(t)

as:

(t)

= αHP

(t−1)

+ (1 − α)SvmOut

(1)

where SvmOut

is deﬁned on the basis of the classiﬁer

output as:

SV MOut











1 if current output of SVM = “A”;

0.5 if current output of SVM = “B”;

0 if current output of SVM = “C”.

In Eq. 1, α is a weighting factor that ponders the

inﬂuence of past observations. In this work we set

α = 0.3 that is sufﬁcient to avoid abrupt variations

and isolated pose estimation errors.

To deal with the uncertainty and vagueness in this

process we use a linguistic variable called “Attention”

and divide it into “High”, “Medium” and “Low” val-

ues (see Fig. 2). This variable will take as input values

the measures of face attention estimation considered

by HP (Eq. 1). In ﬁgure 2 it is possible to see the

labels for the variable “Attention”.

3.2 Fuzzy System for Interest

Estimation

Once the three linguistic variables have been deﬁned,

the rules base that integrates them are explained in

this section. The idea that governs the deﬁnition of

the rules base is dominated by the value of the vari-

able Attention. If the attention has an high value the

possibility of interest is also high depending on the

distance and the angle of the person to the robot. If

the attention is medium then the possibility of interest

has to be decrease but like in the former case depend-

ing on the distance and angle. Finally if the attention

is low, it means that the person is not looking at all to

the area where the robot is located and the possibility

of interest is deﬁned as low or very low depending on

the other variables. The rules for the case in which

Attention is High are shown by Table 1. The other

cases are expressed in a similar way using the appro-

priate rules. The output linguistic variable is Interest

that has the ﬁve possible values shown by Figure 2(d).

Table 1: Rules in the case of high Attention.

IF THEN

Attention Distance Angle Interest

High Low Left High

High Low Center Very High

High Low Right High

High Medium Left Medium

High Medium Center High

High Medium Right Medium

High High Left Low

High High Center Medium

High High Right Low

Finally to compute the value of possible interest, a

fuzzy inference process is carried out using the opera-

tor minimum as implication operator. Then the output

fuzzy sets are aggregated and the overall output is ob-

tained. The overall output fuzzy set can be understood

as a possibility distribution of the interest of the per-

son in the [0, 1] interval. Therefore values near to 1

mean a high level of interest and vice versa.

A FUZZY SYSTEM FOR INTEREST VISUAL DETECTION BASED ON SUPPORT VECTOR MACHINE

185

Figure 4: Interest according distance and attention. Frames in each row (a, b and c), (d, e and f ) and (g, h and i) show the

person at different distances while frames in each column (a, d and g), (b, e and h) and (c, f and i) show the person with

different attention.

4 EXPERIMENTATION

A broader experimentation has been done to validate

our system. All of these experimentations results

were very satisfactory in respect to interest estimation

using our system. Because of space reasons we opted

to describe in detail only one of the experimentations.

To perform the stereo process we have used images of

size 320x240 and sub-pixel interpolation to enhance

the precision in the stereo calculation. The operation

frequency of our system is about 30 Hz without con-

sidering the time required for stereo computation.

Regarding the interest estimation, we have

checked that the interest degree assigned to each

tracked person increases and decreases dynamically

accordingly to the behavior of the person in relation

to the robot. To test it, one person has been recorded

moving in front of the robot in a manner that it was

possible to have frames of the person in all the situ-

ations regarding “Attention” and “Distance”. Frames

from this video can be seen in Fig. 4. In this ﬁgure,

frames a, b, and c show the person close to the robot

(”Low Distance”) and looking in different ways. In-

terest is higher when when person looks towards the

robot. In frames d, e and f it is possible to observe

the same situation but at a higher distance (”Medium

Distance”). Therefore the ﬁnal interest value com-

puted is slightly lower. In frames g, h and i we have

the same person in the same situations (in respect to

where he is looking at) but at even a higher distance

from the robot. Therefore the ﬁnal interest value com-

puted is even lower. It is also possible to observe that

when the person is looking approximately the same

way (in frames a, d, g person is showing low atten-

tion, in frames b, e, h person is showing medium at-

tention and in frames c, f , i he is showing high atten-

tion) interest varies usually according distance. The

closer to the robot the higher the interest.

As it was expected, the higher value of interest

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186

Figure 5: Graphs that show the variation of variables “Attention” (a), “Distance” (b), “Angle” (c) and “Interest” (d) during the

video referred in Section 4.

is achieved in frame c, because the person is look-

ing directly at the robot and it is very close to it. On

the other hand, in frame g the person is not paying

any attention to the robot, moving at a distance far-

away from it. Therefore the lowest interest value is

achieved in this frame.

It is also possible to observe in the graphs showed

in Fig. 5 the variation of variables “Attention”, “Dis-

tance” and “Angle” in Fig. 5.a, Fig. 5.b and Fig. 5.c

respectively during the whole video. The forth graph

Fig. 5.d is the interest computed also for the whole

video. It is also possible to observe in these graphs

the relationship among the interest computed and the

other variables.

In order to better understand the per-

formance of the system, several videos

are available in the following web site

http://decsai.ugr.es/∼ruipaul/interest.htm.

5 CONCLUSIONS AND FUTURE

WORK

In this paper we have shown a system for detecting,

tracking and estimating the interest of the people in

the surroundings of a mobile robot, using stereo vi-

sion, head pose estimation by SVM and fuzzy logic.

As a ﬁrst step, the method for people detection and

tracking has been brieﬂy shown. While a person is

being tracked, the fuzzy system computes a level of

possibility about the interest that this person has in

interacting with the robot. This possibility value is

based on the position of the person in relation with

the robot, as well as on an estimation of the atten-

tion that the person pays to the robot. To examine the

attention that a person pays to the robot we analyze

in real time the head pose of the person. This analy-

sis is solved by a view based approach using Support

Vector Machines. Thanks to SVM head pose can be

detected achieving a great percentage of success that

A FUZZY SYSTEM FOR INTEREST VISUAL DETECTION BASED ON SUPPORT VECTOR MACHINE

187

is no dependent of the morphological features of the

heads. The experimentation shows that the system is

able to detect the persons present in its vicinity, track

their motions and give a value of possible interest on

the interaction of the persons with the robot.

The proposed method can be easily updated in

future works to analyze other types of input data as

sounds or laser range ﬁnder. Also, the degree of in-

terest will be useful to plan the actions of the robot

towards the persons in order to allow a more natural

human-robot interaction.

ACKNOWLEDGEMENTS

This work has been partially supported by the Span-

ish MEC project TIN2006-05565 and Andalusian Re-

gional Government project TIC1670.

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