HYBRID DYNAMIC SENSORS CALIBRATION FROM

CAMERA-TO-CAMERA MAPPING : AN AUTOMATIC APPROACH

J. Badri, C. Tilmant, J.-M. Lavest

LASMEA, Blaise Pascal University, 24 avenue des Landais, Clermont-Ferrand, France

Q.-C. Pham, P. Sayd

CEA, LIST, Atomic Energy Commission, Saclay, France

Keywords:

Camera-to-camera calibration, visual servoing, dynamic camera, video surveillance.

Abstract:

Video surveillance becomes more and more extended in industry and often involves automatic calibration

system to remain efﬁcient. In this paper, a video-surveillance system is presented that uses stationary-dynamic

camera devices. The static camera is used to monitor a global scene. When it detects a moving object,

the dynamic camera is controlled to be centered on this object. We describe a method of camera-to-camera

calibration in order to command the dynamic camera. This method allows to take into account the intrinsic

camera parameters, the 3D scene geometry and the fact that the mechanism of inexpensive dynamic camera

does not ﬁt the classical geometrical model. Finally, some experimental results attest the accuracy of the

proposed solution.

1 INTRODUCTION

Video surveillance is everywhere : banks, airports,

stores, parking lots. Recently, surveillance compa-

nies want simultaneously to monitor a wide area with

a limited camera network and to record identiﬁable

imagery of all the people passing through that area.

The camera choice is different if the goal is to super-

vise a large scene or to acquire high resolution images

of people. Indeed, in the second case, it is necessary

to use a camera with a highly zoom. But, a camera

with zoom allows only to monitor a small area. If

they want to supervise the same area that a wide an-

gle camera network, surveillance companies need a

large number of zoomed camera : it is too expensive.

In a recent past, to solve this problem, people pro-

posed to combine static cameras with dynamic cam-

eras. Indeed, it is possible to control the angle of ro-

tation of the dynamic camera (pan and tilt angles) and

the zoom. In practice, the system proceeds as follows.

A scene event as a moving subject is detected and lo-

cated using the static camera. The dynamic camera

must be controlled with the informations extracted

from the static camera in order to adjust its pan, tilt

and zoom such as the object of interest remains in

the ﬁeld-of-view. Then, high resolution image can be

recorded in order to apply face or gesture recognition

algorithm, for example.

The main problem to solve with such a device is

how to control the dynamic camera parameters from

the information of the object position extracted in

the static camera. These last years, two approaches

emerged. Either, each camera is calibrated in order to

obtain the intrinsic and extrinsic camera parameters

before to ﬁnd a general relation between 2D coordi-

nates in the static camera and the pan and tilt angles,

like (Horaud et al., 2006) and (Jain et al., 2006). Or

cameras are not calibrated like (Zhou et al., 2003) and

(Senior et al., 2005). (Zhou et al., 2003) and (Senior

et al., 2005) learned a look-up-table (LUT) linking

several positions in the static camera with the corre-

sponding pan-tilt angles. Then, for another point, they

estimate the corresponding pan-tilt angles from inter-

polation using the closest learned values.

In order to position the presented paper, we brieﬂy

explain the existing works. In the ﬁrst case, (Horaud

et al., 2006) use previous works to calibrate both cam-

eras of their stereo-vision system. (Jain et al., 2006)

preferred to calibrate separately their cameras. Most

existing methods for calibrating a pan-tilt camera sup-

pose simplistic geometry model of motion in which

axes of rotation are orthogonal and aligned with the

498

Badri J., Tilmant C., Lavest J., Pham Q. and Sayd P. (2007).

HYBRID DYNAMIC SENSORS CALIBRATION FROM CAMERA-TO-CAMERA MAPPING : AN AUTOMATIC APPROACH.

In Proceedings of the Second International Conference on Computer Vision Theory and Applications - IU/MTSV, pages 498-504

Copyright

c

SciTePress

optical center ((Barreto et al., 1999), (Basu and Ravi,

1997), (Collins and Tsin, 1999), (Woo and Capson,

2000)). If these assumptions can be suitable for ex-

pensive mechanisms, they are not sufﬁciently accu-

rate to model the true motion of inexpensive pan-tilt

mechanisms. In reality a single rotation in pan rota-

tion induces a curved movement in the image instead

of straight line.

Last years, (Shih et al., 1998), (Davis and Chen,

2003) and (Jain et al., 2006) proposed a pan-tilt

camera calibration based on a more complex model.

(Shih et al., 1998) gave the details of calibrating a

stereo head with multiple degrees of freedom, how-

ever orthogonally aligned rotational axes were still

supposed. (Davis and Chen, 2003) proposed an im-

proved model of camera pan-tilt motion and virtual

calibration landmarks using a moving light-emitting

diode (LED) more adapted to the dynamic camera

calibration. The 3D positions of the LED were in-

ferred, via stereo triangulation, from multiple station-

ary cameras placed in the environment.

Recently, (Jain et al., 2006) showed that the tech-

nique of Davis and Chen can be improved. Their new

method calibrates more degrees of freedom. As with

other methods you can calibrate the position and ori-

entation of the camera’s axes, but you can also cal-

ibrate the rotation angle. It is more efﬁcient, more

accurate and less computionally expensive than the

method of Davis and Chen. Actually, (Jain et al.,

2006) mean to be the only one to propose a method

without simplistic hypothesis. The step of calibration

involves the presence of a person to deal with the cal-

ibration marks. So, this method can not be used in the

goal of a turnkey solution for a no-expert public.

Now, methods based on the no-direct camera cal-

ibration are focused. Few people explore this ap-

proach. (Zhou et al., 2003) use collocated cameras

whose viewpoints are supposed to be identical. The

procedure consists of collecting a set of pixel location

in the stationary camera where a surveillance subject

could later appear. For each pixel, the dynamic cam-

era is manually moved to center the image on the sub-

ject. The pan and tilt angles are recorded in a LUT in-

dexed by the pixel coordinates in the stationary cam-

era. Intermediate pixels in the stationary camera are

obtained by a linear interpolation. At run time, when

a subject is located in the stationary camera, the cen-

tering maneuver of dynamic camera uses the recorded

LUT. The advantage of this approach is that calibra-

tion target is not used. This method is based on the

3D information of the scene but the LUT is learned

manually.

More recently, (Senior et al., 2005) proposed a

more automatic procedure than (Zhou et al., 2003).

Figure 1: Our system of collocated cameras : the static cam-

era is on the left and the dynamic camera is on the right.

To steering the dynamic camera, they need to know a

sequence of transformations to allow to link a position

with the pan-tilt angles. These transformations are

adapted to pedestrian tracking. An homography links

the foot position of the pedestrian in the static cam-

era with the foot position in the dynamic camera. A

transformation links the foot position in the dynamic

camera with the head position in the dynamic camera.

Finally, another transformation, a LUT as (Zhou et al.,

2003), links the head position in the dynamic cam-

era with pan-tilt angles. These transformations are

learned automatically from unlabelled training data.

The main method default relies on the training data.

If this method is used for a turnkey solution for a no-

expert public and unfortunately the scene changes, it

is impossible that a no-expert public could constitute

a good and complete training data in order to update

the system.

A solution in the continuity of (Zhou et al., 2003)

and (Senior et al., 2005) works is proposed. Indeed,

(Jain et al., 2006) need the depth information of the

object in the scene. So they need to use stereo triangu-

lation. But, like in ﬁgure 1, this system is composed

of two almost collocated cameras.

Moreover, for an automatic and autonomous sys-

tem, solutions proposed by (Jain et al., 2006) and (Se-

nior et al., 2005) are not usable. In fact, they need

an expert knowing precisely how to use a calibration

target (Jain et al., 2006) or how to extract the good

informations to make the training data (Senior et al.,

2005).

In this paper, an automatic and autonomous solu-

tion is presented for an uncalibrated pair of cameras.

The solution adapts automatically to its environment.

In fact, if the pair of cameras are in a changing envi-

ronment, this solution can be restarted regularly.

HYBRID DYNAMIC SENSORS CALIBRATION FROM CAMERA-TO-CAMERA MAPPING: AN AUTOMATIC

APPROACH

499

2 AUTOMATIC SUPERVISED

MULTI-SENSOR

CALIBRATION METHOD

This section describes the calibration algorithm. The

method is presented in two main steps. First, the

computation of a camera-to-camera mapping (LUT)

is explained in order to link a position in the scene

with pan-tilt parameters (ﬁgure 2). Secondly, the

method to extend LUT is proposed in order to get

dense matching.

Figure 2: Scheme of the static-dynamic cameras devices.

2.1 Camera-to-Camera Calibration :

3D Scene Constraints Integration in

LUT Computation

Let us deﬁne the notations used in the following.

• I

s

: image of the static camera;

• N

i

s

: node i of the regular grid in I

s

;

• N : list of nodes N

i

s

;

• (α, β) : current pan-tilt angles of the dynamic

camera;

• I

d

(α, β) : image of the dynamic camera depending

of (α, β);

• N

i

d

: node i of the regular grid in I

d

(α, β);

• C

d

: center of I

d

;

The ﬁeld-of-view of I

d

depends on the pan-tilt angles

and the zoom. In this case, the system works for a

constant zoom. The ﬁeld-of-view of I

s

is 2.5 times

magniﬁcation of I

d

.

Let us denote the n

s

coordinates of N =

N

0

s

, N

1

s

, ..., N

n

s

−1

s

in I

s

. The link between the n

s

Figure 3: Grid applied to the image of the static camera.

nodes N

i

s

and the pan-tilt parameters must be known

such as N

i

s

is map to C

d

. For each node N

i

s

, a visual

servoing loop in the dynamic camera is used to learn

automatically the LUT.

Principal steps of our method :

1. Grid deﬁnition;

2. Initialisation on a node N

i

s

;

3. For each node N

i

s

in the static camera :

(a) Selection of images I

s

and I

d

(α, β) to be com-

pared

(b) Extraction and robust matching of interest

points between I

s

and I

d

(α, β)

(c) Computation of an homography H between in-

terest points of I

s

and I

d

(α, β)

(d) Computation of the N

i

s

coordinates in I

d

(α, β) :

N

i

d

= H × N

i

s

(e) Command of the dynamic camera in order to

insure that N

i

d

catch up with C

d

(f) Process N

i

s

until you reach the condition

|N

i

d

−C

d

| < ε

otherwise stop the loop after m loops

4. Go to the step (3) to process the node N

i+1

s

;

At step (1), a regular grid is applied to the image

of the static camera (ﬁgure 3). The choice of the grid

is made such as there is a common part of the ﬁeld-

of-view of I

d

(α, β) between two neighbour nodes.

At the step (2), a start node N

0

s

must be selected.

To ﬁnd this initial point, angles are randomly selected

to steer the dynamic camera. We stop when the ﬁeld-

of-view of the dynamic camera falls in the neighbour-

hood of a node.

VISAPP 2007 - International Conference on Computer Vision Theory and Applications

500

The ﬁeld-of-view of the dynamic camera is

smaller than the static one (ﬁgure 4). In order to op-

timize the matching result, a small image is extracted

at the step (3a) from the complete image I

s

around the

node N

i

s

to process.

For the step (3b), the scale-invariant feature trans-

form (SIFT) method proposed by (Lowe, 1999) for

extracting and matching distinctive features from im-

ages of static and dynamic cameras is used. The fea-

tures are invariant to image scale, rotation, and par-

tially invariant to changing viewpoints, and changing

in illumination.

At the step (3c), let us assume that locally the area

in the static and dynamic cameras can be approximate

by a plane. Locally, the distortion in I

s

can be consid-

ered insigniﬁcant. So, the homography H is searched

that best matches a set of points extracts in (3b). As

set of correspondences contains a lot of outliers, the

homography H is robustly estimated with a RANSAC

procedure. An homography is randomly computed

from only four points, and test, how many other points

satisfy it many times. The optimal solution is the

homography which has the highest number of good

points.

When the coordinates of N

i

s

in I

d

are known, the

parameters (α, β) of the dynamic camera must be es-

timated in order to insure the convergence of N

i

d

to the

center C

d

. We use a proportional controller based on

the error between the coordinates of N

i

d

and the co-

ordinates of C

d

to minimize the criterion of step (3f).

We assume that the pan-tilt axes and the coordinates

axes are collocated. So we can write the following

relation.

α

β

=

K

x→α

0

0 K

y→β

∆x

∆y

The error (∆x, ∆y) correspond to the coordinates

of N

i

d

−C

d

. As we are in a static training problem, a

proportional controller with the coefﬁcient K

x→α

for

the horizontal direction and K

y→β

the vertical direc-

tion is sufﬁcient.

This procedure is repeated as long as |N

i

d

−C

d

| < ε

is not achieved. If the system diverges, it stops after

m loops.

After convergence, a new node N

i+1

s

is processed.

To steer the dynamic camera in the neighbourhood

of the next point, the needed angles are estimated

with linear interpolation based on the knowledge of

the previous points. For the initial node N

0

s

, no pre-

vious nodes is known to located the next one, but

the visual servoing deﬁnes a local approximation be-

tween the variation of angles and the variation of im-

age points. The linearization is accurate enough to

estimate a rough position of the searched node.

2.2 Management of Failures :

Expansion of LUT

When the procedure seen section 2.1 is completed,

sometimes the system fails to learn the relation be-

tween some nodes N

i

s

and the pan-tilt parameters.

These failures result from the lack of interest points

around some nodes because the scene is too homo-

geneous like a part of a white wall (see top right in

ﬁgure 3). In this case, we have not enough points to

compute the homography.

In order to complete the LUT for all the pixels of

the static image, an approximation of the missing data

is searched. In such interpolating problems, Thin-

Plate-Sline (TPS) interpolation, proposed by (Book-

stein, 1989), is often preferred to polynomial inter-

polation because it obtains similar results, even when

using low degree polynomials and avoiding Runge’s

phenomenon for higher degrees (oscillation between

the interpolate points with a big variation). A TPS

is a special function deﬁned piecewise by polynomi-

als. TPS need a training step : data which are learned

during the LUT computation. Then, for a more im-

portant grid, the corresponding pan-tilt parameters are

estimated.

Figure 5: Result of the TPS interpolation method. Plus

correspond to the learned correspondences of the LUT and

used as training data for TPS interpolation. Points corre-

spond to the result of TPS interpolation with a more com-

plete grid than the initial grid used to learn the LUT.

3 RESULTS AND EXPERIMENTS

Cameras of the AXIS company are used. The image

resolution used is 640 × 480 pixels for I

s

and 704 ×

HYBRID DYNAMIC SENSORS CALIBRATION FROM CAMERA-TO-CAMERA MAPPING: AN AUTOMATIC

APPROACH

501

Table 1: Error in pixels committed for the horizontal (x) and

vertical (y) axes when the dynamic camera is controlled.

There are three conﬁgurations : learned scene (LS) only,

learned scene with an unknow object and learned scene with

a person. Error is estimated to four positions of subjects in

the image (case 1, ... case 4).

LS only LS + object LS + person

x y x y x y

Case 1 1.9 8.8 1.7 11.6 1.9 14.8

Case 2 2.7 2.7 5.3 4.5 5.0 10.6

Case 3 3.6 3.6 2.2 5.5 7.6 10.3

Case 4 18.9 33.4 10.6 15.2

4 CONCLUSION AND

PERSPECTIVES

In this paper, an algorithm of a camera-to-camera cal-

ibration was presented in order to steer a dynamic

camera using informations of the static camera.

Method accuracy reaches the minimal mechanical

step allowed by the dynamic camera device. More-

over, the accuracy decreases when the dynamic cam-

era is centered on an unknow subject. But it is sufﬁ-

cient to initialize a tracking phase with the dynamic

camera.

In the future, grid deﬁnition and zoom integration

must be changed.

Actually, a regular grid without relation with the

3D content of the scene is used. A grid based on the

results of the SIFT method is more interesting. For

instance the choice of the points as a gravity center of

an area with a good density of interest points in order

to make more robust our method can be used.

Secondly, if people high resolution images are

recorded during a tracking step, it is necessary to inte-

grate the zoom to this system. So, for each zoom, the

same procedure will be used to learn the correspond-

ing LUT and then to construct a 3D LUT giving the

relation between the coordinates of point in the static

camera with the pan-tilt-zoom parameters of the dy-

namic camera.

REFERENCES

Barreto, J. P., Peixoto, P., Batista, J., and Araujo, H. (1999).

Tracking multiple objects in 3d. IEEE Intelligent

Robots and Systems.

Basu, A. and Ravi, K. (1997). Active camera calibration

using pan, tilt and roll. IEEE Transactions on Systems

Man and Cybernetics.

Bookstein, F. L. (1989). Principal warps: Thin-plate splines

and the decomposition of deformations. IEEE Trans-

actions on Pattern Analysis and Machine Intelligence.

Collins, R. T. and Tsin, Y. (1999). Calibration of an outdoor

active camera system. IEEE Computer Society.

Davis, J. and Chen, X. (2003). Calibrating pan-tilt cameras

in wide-area surveillance networks. In ICCV ’03: Pro-

ceedings of the Ninth IEEE International Conference

on Computer Vision. IEEE Computer Society.

Horaud, R., Knossow, D., and Michaelis, M. (2006). Cam-

era cooperation for achieving visual attention. Ma-

chine Vision Application.

Jain, A., Kopell, D., Kakligian, K., and Wang, Y.-F.

(2006). Using stationary-dynamic camera assemblies

for wide-area video surveillance and selective atten-

tion. In IEEE Computer Vision and Pattern Recogni-

tion. IEEE Computer Society.

Lowe, D. G. (1999). Object recognition from local scale-

invariant features. In Proc. of the International

Conference on Computer Vision ICCV, Corfu, pages

1150–1157.

Otsu, N. (1979). Aa threshold selection method from grey

scale histogram. IEEE Transactions on Systems Man

and Cybernetics.

Senior, A. W., Hampapur, A., and Lu, M. (2005). Acquiring

multi-scale images by pan-tilt-zoom control and auto-

matic multi-camera calibration. In WACV-MOTION

’05: Proceedings of the Seventh IEEE Workshops on

Application of Computer Vision (WACV/MOTION’05)

- Volume 1. IEEE Computer Society.

Shih, S., Hung, Y., and Lin, W. (1998). Calibration of an

active binocular head. IEEE Transactions on Systems

Man and Cybernetics.

Woo, D. and Capson, D. (2000). 3d visual tracking using a

network of low-cost pan/tilt cameras. IEEE Computer

Society.

Zhou, X., Collins, R., Kanade, T., and Metes, P. (2003). A

master-slave system to acquire biometric imagery of

humans at distance. In ACM International Workshop

on Video Surveillance. ACM Press.

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