CLEAR IMAGE CAPTURE

Active Cameras System for Tracking a High-speed Moving Object

Hiroshi Oike, Haiyuan Wu, Chunsheng Hua and Toshikazu Wada

Department of Computer and Communication Sciences, Wakayama University, 930 Sakaedani, Wakayama City 640-8510, Japan

Keywords:

Object tracking, Binocular Active camera, Clear image.

Abstract:

In this paper, we propose a high-performance object tracking system for obtaining high-quality images of a

high-speed moving object at video rate by controlling a pair of active cameras that consists of two cameras

with zoom lens mounted on two pan-tilt units. In this paper, “high-quality image” implies that the object

image is in focus and not blurred, the size of the object in the image remains unchanged, and the object is

located at the image center. To achieve our goal, we use the K-means tracker algorithm for tracking objects in

an image sequence captured by the active cameras. We use the results of the K-means tracker to control the

angular position and speed of each pan-tilt-zoom unit by employing the PID control scheme. By using two

cameras, the binocular stereo vision algorithm can be used to obtain the 3D position and velocity of the object.

These results are used in order to adjust the focus and zoom. Moreover, our system allows the two cameras

to gaze at a single point in 3D space. However, this system may become unstable when the time response

deteriorates by excessively interfering in a mutual control loop or by strict restriction of the camera action. In

order to solve these problems, we introduce the concept of reliability into the K-means tracker, and propose a

method for controlling the active cameras by using relative reliability. We have developed a prototype system

and conﬁrmed through extensive experiments that we can obtain focused and motion-blur-free images of a

high-speed moving object at video rate.

1 INTRODUCTION

It is likely that a captured image may include a blurred

object if the object is moving at a high speed and the

camera is relatively stable. In such cases, we will lose

important information (e.g., the object’s edge and col-

ors), which is required in several computer vision re-

searches. With regard to the problems in recognizing

and understanding high-speed moving objects, cap-

turing a high-quality image is as important as analyz-

ing the captured object image.

To solve these problems, we propose a high-

performance object tracking system for obtaining

high-quality images of a high-speed moving object at

video rate.

1.1 Related Work

Related works on active vision tracking systems have

been developed by many researchers. The following

are examples of some representative researches.

(a) Active tracking system using vision chip (Ko-

muro et al., 2003).

(b) Active tracking system using high-speed cameras

(Okada et al., 2004).

viewpoint pan-tilt-zoom camera (Matsuyama

et al., 2000).

(d) Active tracking system using binocular stereo

heads (Bjorkman and Eklundh, 2002).

(e) Binocular pursuit system (Coombs and Brown,

1993).

(f) Visual closed-loop system using Dynamic effect

(Corke and Good, 1996).

The system in (a) may be the fastest driven track-

ing system in the world. Since it uses a special sensor

(Vision Chip), the resolution of the obtained image

is quite low. Further, the system can only pursue an

object in an illumination-controlled indoor room.

The system in (b) was constructed using a high-

speed camera, and can thus only obtain very dark im-

ages due to the short exposure time.

The system in (c) uses an ﬁxed viewpoint pan-

tilt-zoom camera (hereafter referred to as an FV-PTZ

Oike H., Wu H., Hua C. and Wada T. (2007).

CLEAR IMAGE CAPTURE - Active Cameras System for Tracking a High-speed Moving Object.

In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, pages 94-102

DOI: 10.5220/0001647600940102

 SciTePress

camera) similar to our system. Due to the use of back-

ground subtraction as the object tracking algorithm,

the active camera must stop its motion for image cap-

ture. Therefore, moving target images captured by

this system may appear blurred or out of focus. More-

over, the tracking performance of this system is a few

dozen degrees per second.

System (d) was constructed using binocular cam-

eras similar to our system. The advantage of this sys-

tem is that it seldom fails in tracking a target that is

at a different depth when compared to its surrounding

objects. This is a fast-driven system; however, it is

very expensive because it uses very complex special

hardware.

In (e), the system was constructed using binocu-

lar camera head ﬁxed on the robot arm, and In (f), it

presented the effect of the introduction the dynamic

control (like feed-forward control) into closed-loop

tracking system. In these manuscripts, the methods

of object tracking are not described clearly.

As is evident from the details stated above, the re-

lated works require complex, expensive, and special

hardware; in addition, their operation is seldom sta-

ble in a real environment. Moreover, their systems

obtain target object images regardless of their quality,

because the architecture of these systems is based on

the concept of only tracking the object.

1.2 Our Approach

In our system, we use two computers and video cam-

eras that are available in the market to obtain the ob-

ject image, track the target object, and control the ac-

tive cameras at 30 fps. In our system, the camera

is controlled such that it moves at the same angular

speed and direction as the target object. Therefore, we

can obtain an image with a blurred background and

clear target at the image center (Fig.1). Additionally,

we use binocular active cameras to track the object.

We can then estimate the 3D position and velocity of

the object. The 3D information can be used for ad-

justing the focus and zoom of the camera. Therefore,

we can obtain target images that are clearer than those

Figure 1: Obtained by conventional method (left) and pro-

posed method (right).

obtained by using only a single active camera. The re-

quirements for our system are as follows:

(1) binocular active cameras to focus their optic axis

on a point in 3D space

(2) target to appear at the center of the images

This is because condition (1) helps us to avoid

the contradiction between detection with two cam-

eras. Epipolar geometry can work stably only if there

is no contradiction between the two cameras.

Condition (2) is required because the view angle

will become narrow when zooming in and the target

will easily escape from the image. In such cases, the

object tracking may completely fail, and thus, there

will be no means to control the cameras.

In fact, it is difﬁcult to estimate the absolute cor-

rect epipolar line because of the errors in object track-

ing or estimation of camera directions. The system

becomes unstable if it is controlled according to an in-

correct epipolar line. Further, the system may become

unstable and lose its smoothness if the time response

deteriorates under the inﬂuence of excessive interac-

tions between the two control loops, or if the actions

of the cameras are overly restricted. If the tracking

system’s action is not smooth, the target in the im-

age will appear blurred and the accuracy of the object

tracking will deteriorate. Therefore, the control of the

active cameras will become increasingly unstable. As

previously described, to focus the optic axis of two

cameras on one 3D point, we must solve the follow-

ing problems.

(A) The information sent from the other camera may

be incorrect or of low accuracy if a tracking failure

occurs.

(B) Excessive constraint from the other camera will

make the tracking system’s action unstable.

The conventional related studies on active vision

tracking systems do not mention the methods for con-

trolling the binocular active camera with an emphasis

on the quality of the captured images by solving prob-

lems (A) and (B).

Therefore, in this paper, we have proposed a new

method for solving these problems and constructing

a high-speed-tracking active camera system, that can

continuously obtain high quality images. Our system

can automatically control the direction, zoom, and

focus of the two cameras to focus on a point in 3D

space.

To solve problem (A), we introduce the concept

of reliability into the K-means tracker and propose a

method to constrain the camera action by using this

reliability, which is based on the calculation of the

distance from the K-means clusters.

CLEAR IMAGE CAPTURE - Active Cameras System for Tracking a High-speed Moving Object

Figure 2: Construction of high-speed-tracking active cam-

eras.

Figure 3: FV-PTZ camera(Active camera).

To solve problem (B), we propose a method for

determining the level of constraint with a relative re-

liability.

By using our proposed methods, the binocular ac-

tive cameras can be smoothly controlled and their op-

tic axes can intersect at a point in 3D space.

2 CONSTRUCTION OF THE

PROPOSED SYSTEM

We construct our high-speed-tracking active cameras

with two active cameras and two computers (Fig.2).

The two computers can communicate with each other

and share their observations.

For the active camera in our proposed system, we

employ a FV-PTZ camera (Fig.3). This camera is cali-

brated such that its optical center corresponds with the

point of intersection of the pan and tilt axes. There-

fore, the optical center of the FV-PTZ camera does

not move if the pan or tilt angle is changed (Fig.4).

Therefore, by using this camera, we can ignore the

movement of the optical center. As a result, it is easy

Figure 4: Particularity of FV-PTZ camera.

to estimate the angular velocity of the target from the

captured image and represent the target velocity by

the angular velocity of the optical center.

In our system, since two cameras are ﬁxed on the

base, as shown in Fig.2, the distance between the

cameras is constant. Therefore, we can only consider

the rotational relationship between the two cameras

and ignore the translation of the optical center.

3 ANALYSIS OF THE OBJECT

TRACKING ALGORITHM ON

THE IMAGE

Numerous powerful algorithms for object tracking

have been developed, such as Condensation(Isard and

Blake, 1998), mean-shift(Comaniciu et al., 2003) and

K-means tracker(C. Hua and Wada, 2006a; C. Hua

and Wada, 2006b).

Condensation is a very robust algorithm that al-

lows for ambiguity in the target position in the image.

However, it cannot accurately estimate the target po-

sition because of this ambiguity.

For controlling the direction of the active cam-

eras to track a moving object, the tracking algorithm

must output a unique result but not an ambiguous

one. Thus, we consider the mean-shift and K-means

tracker algorithms to be suitable for our system.

Authors of mean-shift algorithm claim that it can

adapt to variations in the target shape, color distribu-

tion, and size. However, through our experiments, we

found that tracking becomes unstable if the target size

varies greatly. Another problem is that when the tar-

get is monochromatic and in the plan shape, the mean-

shift algorithm becomes sensitive to the color shift.

We have developed a K-means tracker algorithm

that utilizes K-means clustering by using both the

positive samples of the target and the negative sam-

ples surrounding the target. In this algorithm, the

target feature is composed of the position as well as

its color because the clustering is performed in a 5D

space spanned by 3D color and 2D position param-

eters. This implies that this algorithm can adapt to

not only target position but also the color shift of the

target. Therefore, it can adapt to the shift in the illu-

mination environment caused by changing the camera

direction.

Another feature of the K-means tracker is that it

can adapt to variations in the target shape and size in

the image since it uses a variable ellipse model.

Thus, K-means Tracker is the most suitable algo-

rithm for the active vision tracking system.

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

3.1 Summary of K-means Tracker

In the K-means tracker, robust object tracking is re-

alized by using a variable ellipse model that is up-

dated in each frame according to the clustering re-

sults. The pixels on the variable ellipse contour are

deﬁned as the representative non-target samples, and

the area within the ellipse is the target search area.

In the ﬁrst frame, we manually select some target

cluster centers whose number is roughly the same as

the number of colors contained by the target. In ad-

dition, we select one non-target cluster center b on

the background for the tracking system. Then, the se-

lected non-target center and the centroid c of the tar-

get centers will constitute the initial variable ellipse

model in the form of a circle. The distance kc − bk is

the radius of this circle.

3.1.1 Clustering in the 5d Feature Space

To represent the properties of the target features in

the K-means Tracker, each pixel in an image is de-

scribed by a 5D feature vector f = [k p]

, where,

k = [Y U V ]

describes the color similarity and

p = [x y]

describes the position approximation of

the pixel. The feature vector of the i th target cluster

center is represented as follows:

(i) = [k

(i) p

(i)]

(i = 1 ∼ n). (1)

The feature tor of the j th non-target cluster center on

the ellipse contour is represented as follows:

(j) = [k

(j) p

(j)]

(j = 1 ∼ m). (2)

Here, n and m describe the number of cluster center

of target and non-target, respectively.

To distinguish whether pixel u belongs to the tar-

get center or not, we calculate the distances from f

the target and non-target cluster centers, respectively.

) = min

i=1∼n

{kf

(i) − f

} (3)

) = min

j=1∼m

{kf

(j) − f

} (4)

Here, within the search area, f

describes the feature

vector at pixel u, d

(u) and d

(u) describe the dis-

tances from f

to its nearest target cluster center and

nearest non-target cluster center, respectively.

If d

) < d

), the pixel is detected as a tar-

get pixel; otherwise, it is a non-target pixel.

3.1.2 Updating Variable Ellipse Model

To estimate the search area represented as a variable

ellipse, we represent the equation of ellipse parame-

ters as a relation of the Mahalanobis distance and the

Gaussian probability density function.

[y − c]

−1

[y − c] = J (5)

Where,

J = −2 ln(1 −

100

) (6)

Σ =

y∈S

[y − c][y − c]

. (7)

Equations (5), (6), and (7) indicate that the search

area ellipse will contain P % of the target pixels exist-

ing within the ellipse when applying Gaussian prob-

ability function is applied to ﬁt to the set of target

points (Fig. 5). ). This has the added effect of re-

moving outlying pixels.

Figure 5: Estimation of the ellipse parameter by using Ma-

halanobis distance and Gaussian probability density func-

tion.

The center c of the variable ellipse at the next

frame is calculated as follows:

c =

y∈S

y. (8)

Here, S describes the pixel set inside the ellipse; e de-

scribes the number of target pixels inside the ellipse;

and y = [y

, y

]

describes the target pixels.

In our system, we use c to represent the target cen-

ter. By updating c in every frame, our system can

track the target and estimate its angular velocity.

4 PROCESS FLOW

The K-means tracker discriminates each pixel within

the search area into a target and non-target pixel. In

our method, based on this discrimination, we propose

the concept of reliability into the K-means tracker.

The reliability represents how similarly each pixel be-

longs to each target cluster. With this reliability, our

proposed system can determine which camera tracks

the target more correctly and restrain the action of the

camera with a lower reliability based on the output of

the higher one.

CLEAR IMAGE CAPTURE - Active Cameras System for Tracking a High-speed Moving Object

Figure 6: Flow chart of the our system.

In Fig.6, we show the ﬂowchart of our proposed

active camera system where the constraint on the

camera action is based on the reliability. The relia-

bility of the left and right cameras is described by R

and R

, respectively. The left and right active cam-

eras can be independently controlled using the result

of K-means tracker. In such cases, each camera can

independently track the target, but it cannot automat-

ically control the zoom and focus.

Our system tracks the target in the images cap-

tured from both cameras by using the K-means

tracker. It then estimates the target position in the

each image. With this position information, the di-

rection from each optical center to the target can be

calculated.

With the result of K-means tracker, we can calcu-

late the reliability of each camera. We can then esti-

mate the epipolar line on the lower reliability camera

according to the target position in the image captured

by the higher reliability camera. The ellipse center in

the image taken from the lower reliability camera is

constrained on the estimated epipolar line. Since the

optical axis of the active camera is controlled based on

the target position in the image, the optical axis of the

active camera with the lower reliability is constrained

on the estimated epipolar line. If the optical axis con-

strains the ellipse position excessively, the FV-PTZ

unit response is lost; the velocity and direction of the

active cameras will then be different from those of the

tracked target. Due to these reasons, the tracking sys-

tem may easily become unstable.

To solve this problem, we propose a method in

which the ellipse position is not completely con-

strained on the epipolar line but is weighted by the

higher reliability value of the higher reliability cam-

era.

Figure 7: Ellipse position constraint on the epipolar line

along the vertical direction.

4.1 Calculation of the Reliability

The reliability of each camera is calculated according

to the result of the distance calculated in the feature

space of the K-means tracker.

In this paper, u represents a pixel in the search area

restricted in an ellipse. r(u) represents the reliabil-

ity of u and describes the similarity of target clusters.

r(u) is calculated as follows:

r(u) =

(

)

)+d

)

) < d

))

0 (otherwise).

(9)

Here, the distances of d

) and d

) are calcu-

lated by Eq.(3) and Eq.(4), respectively.

Further, R is the reliability of all pixels in the

search area restricted in the same ellipse, and is cal-

culated by

R =

u=1

r(u). (10)

Here, C is the number of pixels in the ellipse and e is

the number of target pixels. To correct the difference

between ellipse size of the two cameras, R is normal-

ized by e.

4.2 Constraint Ellipse on Epipolar Line

by using Relative Reliability

In our proposed system, based on the results of track-

ing with the high-reliability camera, the ellipse po-

sition is constrained on the epipolar line only in the

low-reliability camera.

In many cases, the two epipolar lines drawn in

each image become horizontal because the active

cameras are ﬁxed on the horizontal base. Therefore,

the ellipse position is constrained on the epipolar line

only along the vertical direction.

In Fig.7, y

is the distance along the vertical di-

rection from the ellipse center c = (c

, c

) to the

epipolar line. y

(x) is the vertical coordinate of the

intersection point of the epipolar line and the verti-

cal line that passes through the ellipse center c. y

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

calculated as follows:

= y

) − c

. (11)

If the ellipse position is constrained on the epipolar

line, only with y

for a single frame, the camera action

may become unstable, as described above. Thus, in

our system, we let the constraint ∆y be calculated by

∆y = wy

. (12)

Here, w is the weight of the relative reliability and is

determined by

w =

+ R

= max{R

, R

}). (13)

Here, R

and R

are calculated by Eq.(10). If the

reliability of the lower reliability camera equals zero,

w becomes 1, and if the reliability of the two cameras

are identical, w becomes almost 0.5.

5 ACTIVE CAMERA CONTROL

In order to controlling the FV-PTZ unit, we employ

the PID control scheme.

P component is assigned as the target angular ve-

locity represented as follow:

objt

= v

Robjt

+ v

camt

. (14)

Here, v

Robjt

and v

camt

represent the relative angular

velocity of the object and the rotational angular veloc-

ity of the active camera at time t. v

Robjt

is computed

by using angle between the object center and the im-

age center which is represented as s.

Robjt

− s

(t−1)

∆t

(15)

Here, ∆t represents the time between continuous two

frames. The system can know v

camt

by response from

PTU controller.

I component rectiﬁes the difference between the

object center and the image center and it calculated

by follow:

dxt

∆t

. (16)

Because the command format for PTU is angular ve-

locity, I component has to be transformed to angular

velocity form.

D component is represented by the angular accel-

eration calculated as follow:

objt

− v

obj(t−1)

∆t

. (17)

Thus, the PID control scheme is suitable for si-

multaneously controlling the angular speed and po-

sition of the pan-tilt unit. Therefore, the PID-based

Figure 8: The Environment of Comparative Experiments.

pan-tilt control is effective for motion synchroniza-

tion between the target and the active camera.

The control value v

is computed by

= K

objt

+ K

dxt

+ K

∆t.

(18)

Furthermore, using the proposed system, we can ob-

tain 3D position information of the target because two

cameras are used. Therefore, the system can automat-

ically control each camera’s zoom and focus based on

the estimated distance from the cameras. In our sys-

tem, zoom is controlled for keeping the resolution of

the target appearance.

Focus is controlled based on the relationship be-

tween the distance and the best focus value which has

already been known by pre-experiment.

6 EXPERIMENT

6.1 Comparative Experiment

We carried out comparative experiments to verify the

performance of our proposed tracking system by com-

paring it with two other methods.

Method 1: Epipolar constraint is not used to

control the active cameras. This implies that the

two active cameras are controlled independently.

Method 2: The epipolar constraint is applied

to the camera with the lower reliability and the

weight factor is set as w = 1.

Method 3: The proposed method. The epipolar

constraint is applied to the camera with the lower

reliability and the weighted quantity of the con-

straint with a relative reliability is used.

Figure 8 shows the environment in which the com-

parative experiments were performed.

The target object is a doll suspended from the ceil-

ing and swinging like a pendulum. In this experiment,

the doll was at a distance of about 4 meters from the

active cameras in the initial state.

We show a part of the sequence obtained in this

experiment as captured by the right camera in Fig.9.

CLEAR IMAGE CAPTURE - Active Cameras System for Tracking a High-speed Moving Object

Figure 9: Several frames of the sequence obtained in the

experiment.

To compare the three methods impartially, we al-

low the tracked target to assume the same motion in

each experiment. We release the target object at the

same height and allow it to move with an inertial mo-

tion three times.

The red lines in Fig.9 indicate the visual line of

the left camera projected onto the image of the right

camera.

To evaluate the performance of: Controlling the

binocular active camera to make the direction of the

cameras intersect at a point in 3D space, we used the

error of the visual lines of the cameras and compared

this among the three methods. As an evaluation mea-

sure, the error between each normalized vector of the

epipolar planes calculated based on each camera was

used. If the visual lines of the cameras perfectly in-

tersect at a point, the error between each normalized

vector will be zero.

We tracked the target object during 120 frames

and calculated the absolute average and deviation of

the errors between each normalized vector. Fig.10

shows a graph that indicates the error between each

normalized vector with time and the absolute average

and deviation of the error is shown in Table 1.

The error between each normalized vector in

Method 3 was less that in Method 1. Therefore, our

proposed method effectively achieves our goal. The

error between each normalized vector in Method 2

was greater than that in the other two methods.

This is because the control of the directions of the

active cameras became unstable since they could not

respond quickly when the estimated ellipse center was

constrained on the epipolar line. Next, we demon-

Table 1: Average and deviation of the error between each

normalized vector(unit: degree).

Method Ave Dev

1 0.84 0.28

2 1.06 0.71

3 0.33 0.20

-4

-3

-2

-1

0 20 40 60 80 100 120

Error(degree)

frame(Time)

Method3

Method2

Method1

Figure 10: The error between each normalized vector.

strate the target pursuit of our proposed system. To

evaluate it, we use the difference between the rota-

tional velocities of the target and the active camera.

This is calculated as the difference between the target

ellipse center axis of the image in the current frame

and that in the previous frame. We call this difference

position error between frames. This number is equal

to zero if the rotational velocity of the target equals

that of one of the active cameras. On the contrary, if

there is a difference between the velocities, the posi-

tion error between frames increases.

In Table 2, we show the average and deviation

of the absolute value of the position error between

frames of both the cameras when experiments were

conducted using Method 1 and Method 3.

The vertical deviation in Method 3 was marginally

greater than that in Method 1 while the horizontal val-

ues were almost constant.

According to the result of this experiment, we ver-

iﬁed that the tracking performance that uses the pro-

posed epipolar constraint method will not degrade.

6.2 Tracking a Human Head

Figure 11 show several sequential frames that track

a human head. The person in these frames walked

Table 2: The average and deviation of the absolute value of

the position error between frames (unit: degree).

Method:direction Ave Dev

1:horizontal 3.8 2.7

3:horizontal 3.7 2.5

1:vertical 3.5 2.2

3:vertical 3.8 2.9

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100

Table 3: The average and deviation of the absolute value of

the horizontal position error between frames in the human

head tracking experiment (unit: degree).

Right camera Left camera Summary

Ave Dev Ave Dev Ave Dev

2.4 2.6 2.1 2.2 2.2 2.4

Figure 11: Several frames in the sequence that track a hu-

man head.

straight from right to left. In frame no. 080, the per-

son was closest to the cameras.

Table 3 shows the average and deviation of the ab-

solute values of the horizontal position error between

frames in the human head tracking experiment.

Because both the velocity and acceleration of the

target were less than those in experiment 6.1, the

tracking error was smaller.

6.3 Zoom and Focus Control

Figure 12 shows several sequential frames that track

a ball with automatic zoom-focus control. The pur-

pose of the zoom and focus control is to maintain the

tracked target in focus with constant resolution.

In order to test the effect of focus control, the tar-

get was defocused in the ﬁrst frame. By comparing

the images of the right camera with those of the left

camera, it was observed that the size of the ball in the

left camera images became smaller than that in the

ﬁrst frame. In contrast, the ball size in the right cam-

era images remained unchanged because of the zoom

control, which automatically zoomed in when the ball

moved away from the camera.

Moreover, the defocus state in the ﬁrst frame was

automatically canceled by the focus control and the

ball in the image was focused.

Figure 12: Several frames in the sequence for tracking a

target in the zoom-focus control experiment.

7 CONCLUSION

In this paper, we have developed a high-performance

object tracking system that can successfully capture

high-quality images of a high-speed moving object at

video rate. To increase the robustness and accuracy of

object tracking in the video image, we introduced the

concept of reliability into the K-means Tracker. In or-

der to follow the movement of the object, two active

cameras were controlled so that the object appeared

at the center of the image plane. This was realized by

positioning the optic axis of the two active cameras at

the center of the object in the 3D space. To achieve

this, we proposed the concept of relaxed epipolar con-

straint between the two cameras based on the reliabil-

ity of object tracking and applied it to the control loop

of the two active cameras. The extensive comparative

experimental results demonstrated the usefulness and

the effectiveness of our proposed method.

ACKNOWLEDGEMENTS

This research is partially supported by the Min-

istry of Education, Culture, Sports, Science and

Technology, Grant-in-Aid for Scientiﬁc Research

(A)(2)16200014, and (C)(2) 18500131.

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