Exploiting Single View Geometry in Pan-Tilt-Zoom Camera

Exploiting Single View Geometry in Pan-Tilt-Zoom Camera
Exploiting Single View Geometry in
Pan-Tilt-Zoom Camera Networks
A. Del Bimbo1 , F. Dini1 , A. Grifoni2 , F. Pernici1
MICC University of Florence, Italy
Thales Security Solutions Florence, Italy
Abstract. PTZ (pan-tilt-zoom) camera networks have an important
role in surveillance systems. They have the ability to direct the attention
to interesting events that occur in the scene. In order to achieve such behavior the cameras in the network use a process known as sensor slaving:
one (or more) master camera monitors a wide area and tracks moving
targets so as to provide the positional information to one (or more) slave
camera. The slave camera foveates at the targets in high resolution. In
this paper, we propose a simple method to solve two typical problems
that are the basic building blocks to create high level functionality in
PTZ camera networks: the computation of the world to image homographies and the computation of camera to camera homographies. The first
one is used for computing the image sensor observation model in sequential target tracking, the second one is used for camera slaving. Finally a
cooperative tracking approach exploiting the use of both homographies
is presented.
In the last few years, with the advent of smart, computer-enabled surveillance
technologies and IP cameras, the use of surveillance systems for security reasons
has exploded. Moreover control equipment such as PTZ cameras (also known
as dome camera) are and will be of invaluable help for monitoring wide outdoor areas with a minimal number of sensors. For these cameras, however, precalibration is almost impossible. In fact, transportation, installation, changes in
temperature and humidity as present in outdoor environments, typically affect
the estimated parameters. Moreover, it is impossible to recreate the full range of
zoom and focus settings. A tradeoff has to be made for simplicity against strict
geometric accuracy.
It is well known that in areas where the terrain is planar the relation between
image pixels and terrain locations or between the image pixels of two cameras, is
a simple 2D homography. While finding at least four well distributed image point
features to compute the world to image mapping is easy in an indoor environment
(provided that calibration grids are pasted on the floor of the room), in outdoor
environments this is proved to be more complicated especially if the scene area is
not sufficiently textured. For the same reason also image to image homographies
between two different views taken from the same camera or different cameras
are not easy to estimate.
In this paper we propose a calibration and a tracking method for PTZ cameras that greatly simplifies cooperative target tracking. The key contributions
M2SFA2 2008: Workshop on Multi-camera and Multi-modal Sensor Fusion
of the paper are threefold: first, we show how to combine single view geometry
and planar mosaic geometry to compute the world to image and the image to
image homography (The first is used for computing image sensor likelihood for
sequential target tracking, the second is used for camera slaving). Second we
show how line features in the mosaic computed with a parameterization with
a minimum number of parameters gives globally better results than using 3D
known coordinate of human-measurable feature points. Third the method can
be used to enhance future surveillance systems to keep track of targets at high
resolution that may not necessarily be captured within one field of view.
Related Work
In the literature, several methods exist to calibrate one or several PTZ cameras.
These methods can be distinguished according to the particular task to perform.
The method [8] can be used to self-calibrate (without calibration targets) a
single PTZ camera by computing the homographies induced by rotating and
zooming the camera. In [6] the same approach has been analyzed considering
the effect of imposing different constraints on the intrinsic parameters of the
camera. They reported that best results are obtained when the principal point
is assumed to be constant throughout the sequence although it is known to be
varying in reality. In [12] a very thorough evaluation of the same method is
performed with more than one hundred images. Then the internal calibration of
the two PTZ camera is used for 3D reconstruction of the scene through essential
matrix and triangulation by using the mosaic images as a stereo pair.
Another class of methods using self-calibration based on moving objects has
been proposed. For example [5] and [14] use LEDs. As the LED is moved around
and visits several points, these positions make up the projection of a virtual
object (modeled as 3D point cloud) with unknown position. However the need
of synchronization prevent the use of the approaches for IP camera networks.
After the VSAM project [2] new methods have been proposed for calibrating
PTZ cameras with simpler and more flexible approaches suitable for outdoor
environment. These are mainly targeted to high resolution sensing of objects at
a distance, and therefore the zoom usage is of mandatory importance in these
methods. Of particular interest are the works [15], [7] and [4] where the PTZ
camera scheduling problem is addressed.
Problem Formulation
In section 3.1 the PTZ camera network with master-slave configuration is defined
in terms of its relative geometry. The section 3.2 describe how to compute this
basic geometry using the single view and the planar mosaic geometry. Finally
in section 4 the estimated geometry is exploited to cooperatively track a target
over an extended area at high resolution.
PTZ Camera Networks with Master-Slave Configuration
PTZ cameras are particularly effective when configured in a master-slave configuration [15]: the master camera is set to have a global view of the scene so
M2SFA2 2008: Workshop on Multi-camera and Multi-modal Sensor Fusion
that it can track objects over extended areas using simple tracking methods with
adaptive background subtraction. The slave camera, can then follow the trajectory to generate close-up imagery of the object. Evidently their respective roles
can be exchanged. The master-slave configuration can be extended to the case
of multiple PTZ cameras. Fig.1 shows the pair-wise relationship between two
cameras in this configuration. H is the world to image homography of the master
camera , H′ is the homography relating the image plane of C with the reference
image plane Π′ of the slave camera C′ and Hk is the homography relating the
reference image plane Π′ with the current image plane of the slave camera. Once
Hk and H′ are known the imaged location x1 of a moving target X1 tracked by
the stationary camera C can be transferred to the zoomed view of C′ by:
Tk = Hk · H′
With this pairwise relationship between cameras the number of possible network
configuration can be calculated. Given a set of PTZ cameras Ci viewing a planar
scene, we define N = {Csi }M
i=1 a PTZ camera network with the master slave
relationship, where M denotes the number of cameras in the network and s
defines the state of each camera. At any given time these cameras can be in one
of two states si = {master, slave}.
The network N can be in one of
Tk = Hk ⋅ H′
2M − 2 possible state configurations.
All cameras in a master state, or all
Π′ cameras in a slave state cannot be deΠ
fined. It is worth noticing that from
this definition more than one camera
can act as a master and/or slave
camera. In principle without any loss
of generality if all the cameras in a
Fig. 1. The pair-wise relationship be- network have an overlapping field of
tween two cameras in master-slave con- view (i.e. they are in a full connected
figuration. The camera C is the tracking topology) the cameras can be set in a
camera and C′ is the slave camera. H master-slave relationship each other
and H′ are respectively the world to im- (not only in a one to one relationage homography and the image to image ship). For example in order to cover
homography. Π is the 3D world plane large areas more master cameras can
while Π′ is the mosaic plane of the slave be placed with adjacent fields of view.
In this case if they act as a master
camera, one slave camera suffice to
observe the whole area. Several master cameras can have overlapping fields of
view so as to achieve higher tracking accuracy (multiple observations of the same
object from different cameras can taken into account to obtain a more accurate
measurement and determine a more accurate foveation by the slave camera).
Similarly, more than one camera can act as a slave camera while just one can be
used as a master for tracking, for example for capturing high resolution images
of moving objects from several viewpoints.
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Minimal PTZ Camera Model Parameterization
We consider the pin-hole camera model projecting the three-dimensional world
onto a two-dimensional image, with fixed principal point, without modelling
the radial distortion. It is assumed that the camera rotates around its optical
center with no translation. The pan and tilt axes are assumed to intersect. The
3 × 3 matrix Ki contains the intrinsic parameters of the camera for the image
taken at time i and the 3 × 3 matrix Ri defines its orientation. It is possible
to model the whole projection as Pi = [Ki Ri 0], where the equality denotes
equality up to a scale factor. As described also in [9] it is possible to derive
the inter-image homography, between image i and image j as: Hji = Kj Rji K−1
i .
Due to the mechanical nature of PTZ cameras it is possible to assume that
there is no rotation around the optical axis: θ = 0. We will assume that the
center of projection lies at the image center, the pan-tilt angles between spatially
overlapping images is small and the focal length does not vary too much between
two overlapping images fi = fj = f . Under these assumptions, the image-toimage homography can be approximated by:
Hji =  0
−ψji −φji
 
f ψji
1 0 h1
−f φji  =  0 1 h2 
h3 h4 1
where ψji and φji are respectively the pan and tilt angles from image j to
image i, [1]. Each point match contributes with two rows in the measurement
matrix. Since there are only four unknowns, (h1 h2 h3 h4 ), two point matches
suffice to estimate the homography. Estimates for ψ, φ and f can be calculated
from the entries of Hji . With this parameterization matching and minimization
are generally more simple than using the full 8 DOF homography. While with
this parameterization calibration parameters are not accurate, it is still possible
to create a wide single view (i.e. a planar mosaic) maintaining the projective
properties of image formation (i.e. straight lines are still straight lines in the
mosaic). This new view, provided that a moderate radial distortion is present,
can be considered as a novel wide angle single perspective image.
Recovering The Homographies. It is well known that given three orthogonal
vanishing points v1 ,v2 ,v3 they can be used to calibrate a natural pinhole camera
computing the focal length (1 DOF) and principal point (2 DOFs). This can be
done referring to the image of the absolute conic (IAC) ω using the following
constraints [10]: v1 ωv2 = 0, v2 ωv3 = 0, v3 ωv1 = 0. Other constraints on ω
can be obtained from circles [13] [3] and can be exploited for example in the
case of sport video analysis. The ω is responsible for internal camera parameters
according to: ω = K−T K−1 . However as shown below we don’t need to compute
explicitly the entries of K for recovering the homographies. When ω and the
vanishing line l∞ of a 3D world plane are known it is possible to compute up
to a similarity transformation the metric structure of the plane. The rectifying
homography [10] can be computed from the image of the absolute conic ω and
M2SFA2 2008: Workshop on Multi-camera and Multi-modal Sensor Fusion
the vanishing line l∞ of the scene plane as:
β −1 −α β −1
Hr =
0 ,
where l∞ = (l1 , l2 , 1) is the representation of the vanishing line in homogeneous
coordinates while α and β are two scalars that can be computed from the imaged
circular points i and j. The imaged circular points are two complex conjugate
point pairs (i.e. i = conj(j)) that are responsible for the metric properties of imaged planes. They are the projection of the circular points I and J. The circular
points I and J are in the Euclidean world (the scene plane) at canonical coordinates I = (1, i, 0), J = conj(I). It can be shown that the following relationship
holds [10]: i = H−1
r (1, i, 0) = (α − iβ, 1, −l2 − l1 α + il1 β). So the two scalars α
and β are directly extracted from the first component of i. The vanishing line
l∞ is obtained as l∞ = v1 × v2 , where v1 and v2 are the vanishing points of
the two orthogonal directions in the scene plane. The imaged circular points are
computed as the intersections of l∞ with ω. The transformation of eq.3 relates
the world to the image up to an unknown similarity transformation Hs . The
Hs transformation has 4 DOF: two for translation, one for rotation and one for
scaling. Two correspondences suffice to compute Hs (i.e. the world coordinates of
two points with their projection onto the mosaic). Without any loss of generality
it is possible to choose the first point as the origin O = (0, 0) and the second
point as the distance from the first in the 3D world reference. Operatively, just
a length is measured in 3D. The final world to image homography can finally be
computed as:
H = Hr Hs
It is important, for the accuracy of the computation of the vanishing points,
to define the reference image where to stitch the mosaic. For example it does
not make any sense to choose images where the image plane is either parallel
or orthogonal with scene plane or with any scene plane orthogonal to it. In fact
in these viewing conditions the vanishing points meet at infinity in the image,
producing high uncertainty in their localization.
Fig. 2(b) shows the planar mosaic obtained from a set of images acquired by
a PTZ camera (these images are shown in fig.2(a)). In particular in the same
figure are also shown the three orthogonal vanishing points v1 , v2 , v3 and the
vanishing line l∞ used to obtain the world to image homography H of eq.4.
Fig.2(c) shows the rectified mosaic of the area under surveillance.
For the computation of the inter-image homography, it is necessary to choose
four well spaced pairs of corresponding points or lines in the two mosaics. Due
to the wide angle view of the mosaic, the problem is considerably well posed.
Fig.2(d) show four well distributed pairs of corresponding point features in
the mosaic image of two PTZ cameras viewing a planar scene. Fig.2(e) shows
the slave-camera view of fig.2(d)(top) as seen from the master-camera view of
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Cooperative target tracking
The homographies described in the previous section are now exploited to cooperatively track a target moving in a wide area. The image to world homography
H is used to compute the image sensor likelihood for sequential target tracking
in the master camera and the image to image homography H′ is used for camera
slaving by computing the homography Tk of eq.1 (i.e. to transfer imaged target
position from the master to the slave camera).
In this section it is shown how to compute the time variant homography Hk .
We adopt a SIFT based matching approach to detect the relative location of the
current image wrt the reference image: at each time step we extract the SIFT
features from the current image and match with those extracted from the reference frame obtaining a set of points’ pairs. The SIFT features extracted in the
reference image can be considered as visual landmarks. Once visual landmarks
are matched to the current view, the registration errors between these points are
used to drive a particle filter with state the parameters defining Hk . This allows
to stabilize the recovered motion, characterize the uncertainties and reduce the
area where matches are searched. Moreover, because the keypoints are detected
in scale–space, the scene does not necessarily have to be well–textured which is
often the case of planar man–made scene.
Tracking using SIFT Visual Landmarks Let us denote with Hk the homography between the PTZ camera reference view and the frame grabbed at
time step k. What we want to do is to track the parameters that define the homography Hk , using a bayesian recursive filter. Under the assumptions we made,
the homography of eq.2 is completely defined once the parameters ψk , φk , and
fk are known, we used this model to estimate the homography Hk relating the
reference image plane Π′ with the current image at time k (see fig.1). Thus we
adopt the state vector xk , which defines the camera parameters at time step k:
xk = (ψk , φk , fk ) . We use a particle filter to compute estimates of the camera
parameters in the state vector. Given a certain observation zk of the state vector at time step k, particle filters build an approximated representation of the
posterior pdf p(xk |zk ) through a set of weighted samples {(xik , wki )}i=1
”particles”), where the weights sum to 1. Each particle is thus an hypothesis on
the state vector value, with a probability associated to it. The estimated value of
the state vector is usually obtained through the weighted sum of all the particles.
As any other bayesian recursive filter, the particle filter algorithm requires
a probabilistic model for the state evolution between time steps, from which
a prior pdf p(xk |xk−1 ) can be derived, and an observation model, from which
a likelihood p(zk |xk ) can be derived. Basically there is no prior knowledge of
the control actions that drive the camera through the world, so we adopt a
simple random walk model as a state evolution model. This is equivalent to
assume the actual value of the state vector to be constant in time and rely on a
stochastic noise vk−1 to compensate for unmodeled variations: xk = xk−1 +vk−1 .
vk−1 ∼ N (0, Q) is a zero mean Gaussian process noise with covariance matrix
Q accounting for camera maneuvers.
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The way we achieve observations zk of the actual state vector value xk is a
little more complex and deserves a few more explanations. Let us denote with
S0 = {sj0 }N
j=0 the set of SIFT points extracted from the reference view of the
PTZ camera (let us assume for the moment a single reference view), and with
Sk = {sjk }N
j=0 the set of SIFT points extracted from the frame grabbed at time
step k.
From S0 and Sk we can extract pairs of SIFT points that match (through their
SIFT descriptors) in the two views of the PTZ camera. After removing outliers
from this initial set of matches through a RANSAC algorithm, what remains can
be used as annobservation for the particle
filter. In facts, the set of remaining Ñ
pairs: Pk = (s10 , s1k ), ..., (sÑ
suggests a homography between
the reference view and the frame at time step k, one that maps the points
˜ ˜ ˜
{s10 , ..., sÑ
0 } into {sk , ..., sk }. Thus, there exist a triple (ψk , φk , fk ) which, in the
above assumptions, uniquely describes this homography, and that can be used as
a measure zk of the actual state vector value. To define the likelihood p(zk |xik )
of the observation zk given the hypothesis xik we take into account the distance
between the homography Hik corresponding to xik and the one associated to the
observation zk :
j 2
j=1 (Hk ·s0 −sk )
p(z |xi ) ∝ e− λ
where Hik · sj0 is the projection of sj0 in the image plane of frame k through the
homography Hik , and λ is a normalization constant.
It is worth to note that the SIFT
points on the frame k do not need to
be computed upon the whole frame.
In facts, after the particle filter prediction step it is possible to reduce
H(n-1) m
the area of the image plane where the
H2 m
Hn m I
SIFT points are computed to the area
where the particles are propagated.
This reduces the computational load
of the SIFT points computation and
of the subsequent matching with the
SIFT points of the reference image.
Fig. 3. Each landmark in the database
To increment robustness of the rehas a set of descriptors that corresponds
cursive tracking described above, durto location features seen from different
ing a learning stage a database of the
vantage points. Once the current view
scene feature points is build. SIFT
of the PTZ camera matches an image Il
keypoints extracted to compute the
in the database, the inter-image homogmosaic are merged into a large KDraphy Hlm is used to transfer the current
Tree together with the estimated mo′
view into the reference plane Π .
saic geometry. The match for a SIFT
feature extracted from the current frame is searched according to the Euclidean
distance of the descriptor vectors. The search is performed so that bins are explored in the order of their closest distance from the query description vector,
and stopped after a given number of data points has been considered [11].
M2SFA2 2008: Workshop on Multi-camera and Multi-modal Sensor Fusion
Once the image Il closest to the current view Ik is found the homography G
relating Ik to Il is computed at run time with RANSAC. The homography Hlm
that relates the image Il with the mosaic plane Π′ retrieved in the database is
used to finally compute the likelihood. Eq.5 becomes:
p(zk |xik ) ∝ e− λ
(Hik ·sj0 −Hlm ·G·sjk )
As shown in fig.3 the image points of the nearest neighbor image Il wrt to current
view Il and the current view (i.e. the query to the database) are projected in Π′
to compute the likelihood of eq.6. In particular Im in the figure is the reference
image used to compute the mosaic.
Experimental Results
In order to test the validity of the presented method we have acquired 40 images with two IP PTZ-camera Sony SNC-RZ30 in a master-slave configuration.
Fig.2(a) shows the images used taken from one PTZ camera. The input images
are taken at a resolution of 736 × 544 pixels. The images from the other camera
are not shown. The images has been captured at the minimal zoom of the device
and with a pan and tilt angle increment of respectively ψ = 27.14 and φ = 10.0,
so as to have some overlap between images. For a given image, matches are
searched only at the 8 neighbor images in the grid (apart for those images in the
border of the grid). Once correspondences are formed through RANSAC, the
homographies parameterized by eq.2 are computed and are successively refined
by non linear minimization through bundle adjustment. The RANSAC strategy
successful rejects outliers, even if several moving objects are present in the scene.
The image in the second row, fifth column in the grid of fig.2(a) (i.e. the
reference image) is used to stitch the mosaic so as to avoid degeneracies in the
estimation of the image of the absolute conic ω. Parallel lines have been manually extracted by following the imaged linear boundaries. Fig.2(b) shows the
features point over the image boundaries in the image mosaic representing the
pairs of mutually orthogonal parallel lines used to compute the vanishing points.
The lines are fitted by orthogonal regression. In the same figure it is also shown
the orthocenter p of the vanishing point triangle and the reference image is indicated with a rectangle. Fig.2(c) shows the image mosaic transformed by the
rectifying homography of eq.3. The global Euclidean structure of the 3D world
plane is recovered.
In extended areas, obtaining a ground truth homography is operatively difficult to be made with a rule. This prevents an extensive statistical evaluation in
real scenario. For this reason we preferred to compare the method described with
a conventional method in a real scene. We measured four world point coordinates
which project onto the input image located in the first row, seventh column of
the grid in fig.2(a). Hence we compute the world to image homography which
is used to rectify the mosaic. The world points are distant no more than 15 meters each other. Their positions are computed by measuring the inter-distance
between the 3D marker and then solving for the 3D coordinates. The result is
M2SFA2 2008: Workshop on Multi-camera and Multi-modal Sensor Fusion
shown in fig.2(f), it is evident that the lines of the street in the courtyard are no
more parallel after the rectification.
The right angle of the sidewalk of the courtyard can be used to validate
the accuracy. The angle is measured to be nearly 90◦ by the rectification using eq.3, while the angle in fig.2(f) measures 76◦ . This can be explained by the
fact that the world to image homography is quite accurate locally where the
measurements are taken, while as we move from these position errors increase.
To further appreciate this behavior fig.2(g) shows a regularly grid of equispaced
points backprojected over the imaged planar region in the mosaic. In particular
it is also shown the reference image (the rectangle) and the imaged world points
used to compute the homography with the conventional method. Fig.2(h) shows
a global view of same figure. The imaged grid is very inaccurate outside the reference image. The homography computed by the measured local features does
not give good global results also because large outdoor scene areas may deviate
from being planar.
For testing purposes, a simple algorithm has been developed to automatically
track a single target using the recovered homographies. The target is localized
with the wide angle stationary camera using background subtraction and its
motion within the image is modeled using an Extended Kalman Filter (EKF).
The observation model is obtained by the linearization of eq.4. Images are used
to compute respectively the image to world homography for the master and the
inter-image homography relating the mosaic plane of the two PTZ cameras. Because of the limited extension of the monitored area, a wide angle view of the
master camera suffice to track the target. The feature points of the slave camera
images are used to build the database of SIFT for camera tracking. In fig.4(a)
are shown some frames extracted from an execution of the proposed system: on
the top row is shown the position of the target observed with the master camera,
on the bottom the frames of the slave camera view. The particles show the uncertainty on the position of the target. Since the slave camera does not explicitly
detects the target, the background color similar to the foreground color does not
influence the estimated localization of the target.
A quantitative result for the estimated camera parameters is depicted in
fig.4(b). It can be seen that increasing the focal length usually causes a significant increase in the variance also, which means that the estimated homography
between the two cameras become more and more inaccurate. Observing in detail the particle filter for camera tracking, we noticed from our experiments that
the uncertainty increase with the zoom factor. This is caused by the fact that
features at high resolution that match with those extracted from the reference
image obviously decrease when zooming, causing SIFT match to be less accurate.
However this error remains bounded below certain zoom factors, about 70%.
Summary and Conclusions
In this paper we have shown how to combine single view geometry and planar
mosaic geometry in order to define and solve the two basic building blocks defining PTZ camera networks. Those are the world to image and the inter-image
M2SFA2 2008: Workshop on Multi-camera and Multi-modal Sensor Fusion
homography. The main virtue of our results lies in the simplicity of the method.
Future research will investigate the joint optimization of the single view geometry together with the mosaic registration in terms of the IAC parameterization.
However the most interesting direction (currently under investigation) will use
the presented framework to compute a selective attention strategy, aimed to
track multiple targets by tasking the sensors in the network.
1. A. Bartoli, N. Dalal, and R. Horaud. Motion panoramas. Computer Animation
and Virtual Worlds, 15:501–517, 2004.
2. Collins, Lipton, Kanade, Fujiyoshi, Duggins, Tsin, Tolliver, Enomoto, and
Hasegawa. A system for video surveillance and monitoring: Vsam final report.
Technical report CMU-RI-TR-00-12, Robotics Institute, Carnegie Mellon University, May 2000.
3. C. Colombo, A. D. Bimbo, and F. Pernici. Metric 3d reconstruction and texture acquisition of surfaces of revolution from a single uncalibrated view. IEEE
Transaction on Pattern Analysis and Machine Intelligence, 27(1):99–114, 2005.
4. C. J. Costello, C. P. Diehl, A. Banerjee, and H. Fisher. Scheduling an active camera
to observe people. Proceedings of the 2nd ACM International Workshop on Video
Surveillance and Sensor Networks, pages 39–45, 2004.
5. J. Davis and X. Chen. Calibrating pan-tilt cameras in wide-area surveillance networks. In Proc. of ICCV 2003, 1:144–150, 2003.
6. L. de Agapito, E. Hayman, and I. D. Reid. Self-calibration of rotating and zooming
cameras. International Journal of Computer Vision, 45(2), November 2001.
7. A. del Bimbo and F. Pernici. Distant targets identification as an on-line dynamic vehicle routing problem using an active-zooming camera. IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking
and Surveillance (VS-PETS’05) in conjunction with ICCV, Beijing, China, pages
15–21, October 2005.
8. R. Hartley. Self-calibration from multiple views with a rotating camera. in Proc.
European Conf. Computer Vision, pages 471–478, 1994.
9. R. I. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision.
Cambridge University Press, ISBN: 0521623049, 2000.
10. D. Liebowitz, A. Criminisi, and A. Zisserman. Creating architectural models from
images. In Proc. EuroGraphics, volume 18, pages 39–50, September 1999.
11. D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J.
Comput. Vision, 60(2):91–110, 2004.
12. S. Sinha and M. Pollefeys. Towards calibrating a pan-tilt-zoom cameras network.
P. Sturm, T. Svoboda, and S. Teller, editors, OMNIVIS, 2004.
13. P. P. Sturm and Y. Wu. Euclidean structure from n≥2 parallel circles: Theory
and algorithms. In Proc. of the 9th European Conference on Computer Vision
(ECCV’2006)., pages 238–252, 2006.
14. T. Svoboda, H. Hug, and L. V. Gool. Viroom – low cost synchronized multicamera
system and its self-calibration. In Pattern Recognition, 24th DAGM Symposium,
number 2449 in LNCS,, pages 515–522, September 2002.
15. X. Zhou, R. Collins, T. Kanade, and P. Metes. A master-slave system to acquire
biometric imagery of humans at a distance. ACM SIGMM 2003 Workshop on
Video Surveillance, pages 113–120, 2003.
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Fig. 2. (a): The grid of 40 input images captured at the minimal zoom of the device
and with a pan and tilt angle increment of respectively ψ = 27.14 and φ = 10.0. (b):
The planar mosaic with superimposed the vanishing point triangle v1 , v2 , v3 . (c): The
rectified mosaic. In this picture is shown the rectification through the homography
of eq.3. The global Euclidean structure of the 3D world plane is recovered. (d): Four
well spaced pairs of corresponding points used compute the inter-image homographies
relating the two PTZ camera mosaics. (e): The slave-camera field of regard view as
seen from the master-camera field of regard. (f): Mosaic planar rectification using 3D
known measures. The image used to compute the world to image homography is also
used as a reference image to stitch the mosaic. (g): The reference image (rectangle) in
the mosaic. The figure also shows the backprojection of the four known 3D points, and
the backprojection of a grid of points. (h): A global view of fig.(g) superimposed in the
mosaic (top-right rectangle). The grid is inaccurate outside the reference image.
M2SFA2 2008: Workshop on Multi-camera and Multi-modal Sensor Fusion
Fig. 4. (a): On the top row, the master slave camera tracking a human target. The
world to image homography is estimated from the vanishing points in the mosaic image
and used as observation model in an Extended Kalman Filter. On the bottom row, the
slave camera viewing the target tracked from the master. The particles show the joint
position uncertainty of the target and the slave camera. (b): Nine frames showing the
probability distributions (histograms) of the slave camera parameters. In particular
in each frame are shown (left-to-right top-to-bottom) the current view, the pan, the
tilt and the focal length distributions. As one would expect, uncertainty increase with
zoom factor.
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