REAL TIME FOREGROUND OBJECT DETECTION USING PTZ

CAMERA

Lionel Robinault

1, 2

, Stéphane Bres

3

and Serge Miguet

1

1

LIRIS - Lyon2 University

2

FOXSTREAM - LIRIS, Lyon2 University

Bat. C, 5, Av. Pierre Mendès France

69676 Bron cedex – France

3

LIRIS, INSA Lyon

Bat. Jules VERNE

69 621 Villeurbanne

Keywords: PTZ camera, Background/foreground detection, Gaussian mixture, Image registration.

Abstract : An important research is done to exploit the characteristics of PTZ cameras. These cameras allow

motorized cover a wide field of view. A classic application of these cameras is to image mosaicing. But

they can also be used to track moving objects. In this paper, we present an original approach for performing

the registration, adapted to the case of central projection and a background subtraction algorithms for these

cameras. The background image is iteratively updated and only on the part "seen" by the camera. We have

experimented different segmentation algorithms using our background modeling technique and this

approach makes it possible object tracking in real time for PTZ cameras.

1 INTRODUCTION

The goal of this paper is to present is to detect in

real time the foreground objects from a moving

camera PTZ. Most solutions described in the

literature (Kang 03, Migdal 05, Bevilacqua 06)

requires as a first step, create a complete panorama

of the scene. This panorama is the modeling of

background. During the operation, acquired images

are projected onto the panorama. Moving objects are

segmented from the difference between the

panorama and the projection of the current image.

This approach leads to many problems. Among

other things, the acquisition time of the first stage

and the size memory needed to store the panorama

without loss of information. However, the most

important is the time between the construction of

background model and the acquisition of the current

image. This problem is even more sensitive outdoor

lighting that changes regularly.

In this paper we present a robust background

modeling method adapted to PTZ cameras and does

not require the creation of such a mosaic. The

additional interest of our approach is the reduction

of processing time, in order to deal with real-time

constraints. The first step in our approach relates to

the image registration. We propose a fast image

registration method adapted to the specific case of

central projection. The second step is to update a

background image corresponding only to the field of

view (FOV) of the camera at time t. The rest is

erased from the memory.

This article is structured as follow: in the next

section we present the state of the art and our

approach of image registration. In the section, we

propose an generalization of background modeling

method adapted to PTZ cameras. Then in section 4

we present our experimental results. The conclusion

and the perspectives are presented in section 5.

2 IMAGE REGISTRATION

2.1 State of the Art

Although many solutions have been proposed for

building panoramas, achieving high quality mosaics

609

Robinault L., Miguet S. and Bres S. (2009).

REAL TIME FOREGROUND OBJECT DETECTION USING PTZ CAMERA.

In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications, pages 609-614

DOI: 10.5220/0001796806090614

Copyright

c

SciTePress

in real time remains a very challenging task. The

approaches can be classified according to the

complexity of the model. Moreover, we can

distinguish local vs global methods and direct vs

feature-based approaches. Regarding model

complexity, Bhat and al. (Bhat 00) use a simple

translation motion models for motion segmentation

with a PTZ camera. However, this assumption is

only fulfilled for small tilt angles. More complex

motion models are thus generally proposed, such as

rigid, affine (Szeliski 97, Brown 03) or general

projective models (Bevilacqua 05). In addition, most

cameras deviate from a real pin-hole model due to

radial distortion which becomes more prominent for

shorter focal lengths, and some approaches (Sinha

04) propose to compensate it.

Local approaches aim at determining the model's

parameters for each couple of successive frames,

and consists in a frame to frame (or pairwise)

registration. They are computationally efficient but

this strategy introduces small alignment errors to

accumulate. In particular, these errors become more

evident when a video sequence returns to a

previously captured location (problem known as

"looping path"). Global approaches (Szeliski 97,

Brown 03) formulate the registration problem in

order to solve for all of the camera parameters

jointly, i.e. by requiring that the ends of a panorama

should join up. These kinds of exact optimization

schemes are most of the time not compatible with

real-time purpose, thus making global methods

suitable mainly for batch computation.

Direct (or intensity-based) methods (Szeliski 97 ,

Sinha 04) attempt to iteratively estimate the camera

parameters by minimizing an error function based

on the intensity difference in the area of overlap.

This can be achieved by computing the sum square

difference (SSD) or ZSSD, the correlation

coefficient (CC), the mutual information (MI) and

the correlation ratio (RC). Szeliski and Shum

(Szeliski 97) propose to estimate the registration

homography by iteratively updating a correction

matrix using the SSD. They use an affine model, but

claim that their general strategy can be followed to

obtain the motion parameters associated with any

other motion models (perspective or even including

radial distortion). In addition, they apply global

alignment to the whole sequence of images, which

results in an optimal image mosaic. Direct methods

have the advantage that they use all of the available

data and hence can provide very accurate

registration, but they depend on the fragile

"brightness constancy" assumption, and being

iterative require initialization. Feature-based

methods (Bevilacqua 05, Brown 03) start by

establishing correspondences between points, lines

or other geometrical entities for estimating the

camera parameters. For example, Bevilacqua et. al

(Bevilacqua 05) suggest to match current frame

features (corners) to the background mosaic using

the KLT tracker. They make use of a generic

projective model, and propose to overcome the

"looping path" problem with a feedback registration

correction compatible with real-time requirements.

In their approach no a priori information regarding

the camera parameters or signals (pan/tilt angular

movements). Thus, they use a histogram

specification technique (Azzari 06) to manage

automatic camera exposure adjustments (e.g. AGC)

and environmental illumination changes (e.g.

daytime changes). Brown and Lowe (Brown 03)

propose to match SIFT features between all of the

input images to form the panorama. They make use

of an affine transformation model that they justify

by the partially invariance of SIFT descriptors under

affine change. They use a RANSAC algorithm as a

probabilistic model for image match verification, in

order to discard outliers for the parameters

estimation. Finally, they use bundle adjustment

(Triggs 00) as a global registration scheme to solve

for all of the camera parameters jointly. Although

the approach is efficient, and is able to automatically

images being part of the mosaic, the panorama

computation requires 83 seconds on a 2GHz PC.

2.2 Registration Problem Formulation

Mapping the current frame into a common reference

coordinate system consists in determining the

transformation between the acquired image I and the

previously built panorama P, i.e. finding the

homography between I and P. An homography is

defined as a transformation between two projective

planes. An exhaustive review of the projective

transforms is beyond the scope of the paper, and the

reader can refer to (Faugeras 93).

Projection Model. Using homogeneous

coordinates, the homography corresponds to a linear

transform that can be represented using a 3 × 3

matrix multiplication H. Denoting X =(u,v,1)

T

the

coordinates of a point P

t

in the current image I, the

homography H maps P

t

to P’

t

∈ P, whose

coordinates are X'= (u', v',w')

T

:

VISAPP 2009 - International Conference on Computer Vision Theory and Applications

610

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎝

⎛

×

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎝

⎛

≈

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎝

⎛

×≈

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎝

⎛

111'

'

'

87

654

321

v

u

mm

mmm

mmm

v

u

H

w

v

u

(1)

where ≈ indicates that equation 1holds up to a scale

factor. The equation 1 gives the general form of a

homography, with eight free parameters. However,

the PTZ cameras constitute a special case. For

example, we can assume that the camera's center of

rotation is fixed and coincides with the center of

projection while it is rotating and zooming. Such an

assumption is valid, when the PTZ camera is used

outdoors or in large environments where the shift of

the camera center is small as compared to its

distance to the observed scene. In that case, using a

simplified model removing geometrical or chromatic

distortions, the projection can be expressed as

follows:

111 −−−

⋅⋅⋅⋅⋅=

PP

PI

II

KRRRRKH

ϕ

θθ

ϕ

(2)

R

θ

and R

ϕ

being the rotation matrices in function of

the pan and tilt angles, and K being the simplified

matrix of the intrinsic parameters of the model:

⎟

⎟

⎟

⎠

⎞

⎜

⎜

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=

100

0

0

0

0

vf

uf

K v

u

(3)

where f

u

and f

v

correspond to the focal distance,

given in pixel unit for the axes u and v, (u

0

, v

0

) is the

projection center in the image plane.

Homography Estimation. Considering the two

images I and P that have to be aligned, the

registration problem can thus be formulated as

estimating the homography

H

~

fulfilling the

following equation:

))(,(minarg

~

IHPDH

EH∈

=

(4)

where E is the space search related to the

homography parameters, and D is a dissimalirity

measurement between P and H(I). Solving the

registration problem is thus two-fold. Firstly we

have to define a image similarity measurement

adapted to our context. Secondly we must specify

how the minimization stated in equation 4 is carried

out.

2.3 Our approach

Our problem consists in searching a homography

between two images. In the state of the art, we have

presented two classes of technique: intensity based

and feature based methods. In the case of intensity

based methods, the algorithm of minimization is fast

however the evaluation of the cost function is slow.

For the feature based methods, the research of

interest point is fast but the computation of interest

point features and matching of points is slow. We

propose to mix the two approaches ie minimization

algorithm and extraction of interest points.

As indicated in equation 4, we need to define a

measure of dissimalirity. Usually, the cost function

used is the sum square difference (SSD) measure or

equivalent. The SSD measure is calculated between

all pixels of image. For accelerate the computation

time, we propose to use a cost function based on the

position of the interest point. The first step consists

to calculate the interest points (Harris 88) in two

images. The interest points are calculated once at the

beginning of the algorithm. At each iteration, we

apply the transformation matrix at all points of I.

The cost function is the sum distances between all

points of P and the nearest point of I after

transformation.

(

)

∑

⋅−=

j

IjPi

pHpD

,

2

,

'min

(5)

To optimize the computing time and avoid

seeking the minimum distance between each point,

we define a search area for each point of P. This

search area is defined by the a priori knowledge

(Fig1.)

There are many methods to find the minimum in

a search space, such as simulated annealing and

genetic algorithms. These methods are universally

acknowledged to be less sensitive to local minima.

However, the tests that we have done have shown

that the number of intermediate solution is more

important. For the mimization algorithm, we have

choice a simplex method. The simplex method,

introduced by Nelder and Mead in 1965 (Nelder 65),

is now well-known optimization scheme applicable

in a high-dimension space. It is based on the use of a

polyhedron which dimensions are n+1, n being the

unknown parameters to be determined. Each

iteration updates the polyhedron in order to estimate

the minimum of the cost function.

Moreover, compared to the simplex method, the

conditions of stops on two other methods are more

difficult to determine. The choice of the simplex

method is therefore fully justified. In our

application, the homography has five free

parameters, as stated in equation 2. If none of these

parameters is known, the simplex polyhedron shall

have six vertices. If the parameters of the panorama

REAL TIME FOREGROUND OBJECT DETECTION USING PTZ CAMERA

611

P are known or calculated at time t-1, only three

parameters of image I have to be computed. The

simplex is thus a tetrahedron.

Image P Image I Polygons in I

Figure 1: Search space for ϕp =45°, f

p

=830,

Δθ

=3°,

Δϕ

=3° and and

Δ

f = 100.

3 FOREGROUND

SEGMENTATION

3.1 State of the Art

Several authors (Bhat 00, Kang 03) generate a

preliminary complete (or partial) panorama of the

scene. Then they projecting the current image in the

panorama. There are several representations of

panoramic images. One of them is to project all the

images on a cylinder. This is the solution used in

(Bhat 00).

However, making a complete panorama of the

scene is particularly expensive in terms of memory.

To store all of the scene without losing any

information, it is necessary that the minimum size of

each face of the cube is equal to twice the focal

length expressed in pixels. For example, take a focal

length corresponding to 800 pixels. For a color

image, the required memory space is equivalent to

1600

2

x 3 x 6 or approximately 45 MB. If we use an

algorithm based on Gaussian mixture, a minimalist

solution requires 3 x 16-bit integers by Gaussian and

it takes a minimum of two Gaussians. The memory

is then 540 MB. The memory size is not the only

limiting factor. For the background model to be

meaningful, it is necessary to minimize the time for

modeling the background as well as the computing

time of the difference between current image and the

background. If this time is too long, several factors

make difficult to extract the moving objects. The

change of brightness is also a factor. To

continuously update the panorama is not a good

option. The solution that we propose is, therefore, to

model only the part of the background that is viewed

by the camera in the current image.

Several approaches have been proposed for

background modeling. The goal of this article is not

to make a complete presentation of these methods,

but we can cite three main families. The background

image can be simply built from the previous frame

or from a sliding average on previous images

(Perner 01, Haritaoglu 00). The solution that seems

to give the best results according to the bibliography

is the method of Gaussian mixtures (Stauffer 99,

Lee 05). We will enter with more details into these

different methods.

3.2 Our Approach

The first step was to determine the transformation

matrix between the current and previous images. We

apply the transformation matrix to the background

image I

f

calculated at t-1 that we subtract from the

current image I

c

to obtain the map of foreground

pixels I

m

.

fcm IHII

⋅

−

=

(6)

There are several ways to calculate a background

image. In this article we limit ourselves to one type

of algorithm used by several authors (Stauffer 99,

Lee 05). They model the change of each pixel in the

image over time by using several Gaussian

distributions represented by an average and a

standard deviation. This method is commonly

known as "Gaussian mixture". The number of

distributions used for background modeling depends

on the complexity of the background movements.

The format of the article does not enable us to look

further into the discussion on the relevance of this

model and its parameters. For more information, the

reader will be able to read the article of Stauffer

(Stauffer 99). The tests which we carried out show

that 3 distributions are generally necessary.

In the case of PTZ cameras, our approach

consists in applying the transformation matrix to the

different parameters (average, standard deviation) of

the pixels of the background image. The distribution

of each Gaussian can be accomplished by a bi-linear

interpolation. Our approach makes it possible to use

the transformation matrix on the background image

and to put that back in the context of fixed cameras.

We may use all classic algorithms of segmentation

and identification of motion objects.

It is however important to notice that our

approach does not allow us (under certain

conditions) to segment all the moving objects.

Indeed, the size of the background image being the

same as that of the current image, we lose some

information. That is, the area of the background

image that was present on the previous image and

who has disappeared with the movement of the

VISAPP 2009 - International Conference on Computer Vision Theory and Applications

612

camera. It does not really matter because the camera

movement is mostly linear in time and will therefore

continue in the same direction. The camera does not

change direction any time. What is more

problematic is that a part of the background image is

not available. Is the area of the current image that

was not present in the previous image .

Figure 2: Background image projection on the current

image.

In our example (Fig.2), we applied on the

background image the transformation matrix that

corresponds to the shooting parameters of the

current image. The background image is projected in

the plane of the current image. The rectangle shows

the position of the current image in the plane. We

can notice that a small part of the background image

is outside of the rectangle. This is the part of the

background that is lost. A black area appears inside

the rectangle. This is the part that could not yet be

analyzed by lack of modeling. In the above example

we have voluntarily simulate a major movement in

order to illustrate our point.

4 RESULTS

The following sequence (Fig.3.) corresponds to a

real case. The camera could not give us a reliable

position measurement, we used our registration

technique. We present some images from the movie

with the binarization results. The first column is the

image acquired by the camera. In this example, the

camera is rotating pan in the trigonometric direction.

The overall scene is moving. In this scene, a

pedestrian is also moving. The second column is to

magnify the person in motion. Other columns

correspond to the binarization of various methods.

Column (WR) is the result of the difference, after

binarization, between the current image and

projection of the previous image in the plan of the

current image. The projection matrix is estimated

with our registration method. The column (CP) is

the result of our background model but by using the

parameters of the camera to calculate the projection

matrix. Column (OA) is our approach (ie. image

registration + mixture of gaussian). Compared to

WR, our approach shows the contribution of our

background model. In the case of CP, if the camera

parameters were precise, the results would be

comparable with our approach. However, will

traditional PTZ cameras are not precise. For

example, on the camera Sony RZ25P, the

information of position is updated once time by

second. If the positions taken by the camera are not

just, the object segmentation is not perfect. Our

approach helps to properly segment the pedestrian.

These tests were carried out on a laptop - HP

Pavilion equipped with a 1.8GHz AMD processor

and 1GB RAM. The computing times for 704 x 576

pixels images are 22ms for Gaussian mixtures. They

make possible object tracking in real time.

Frame 311 Ped. WR CP OA

Frame 316 Ped. WR CP OA.

Frame 321 Ped. WR CP OA.

Frame 326 Ped. WR CP OA.

Figure 3: Real case sequence.

5 CONCLUSIONS

In this article we have presented a method for real-

time background substraction adapted to PTZ

cameras. The method we propose is not intended to

achieve a robust panorama. It helps, however, to

quickly calculate the projection between two

successive frames of a video camera PTZ moving.

REAL TIME FOREGROUND OBJECT DETECTION USING PTZ CAMERA

613

After the projection of the image J in the

background of image I, the difference between the

two images permitted the computation of the motion

map. The best results are obtained with mixtures

Gaussian. With the image registration, the

computation time of the motion map is 29ms. The

computation times reduced our method allows

computing time available for other treatments, such

as segmentation. Another advantage of our method

is that it is less sensitive to changing light

conditions. The brightness changes are immediately

integrated as in the case of a fixed camera.

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