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ligence and robotics research, by providing a stan-
dard problem, where a wide range of technologies
can be examined and integrated (Kitano et al., 1997).
RoboCup Rescue project focus on urban disaster res-
cue. It aims at promoting research and development
by involving multi-agent team work coordination.
RoboCup Rescue competitions can be difficult to
monitor and visualize as they comprise a complex and
dynamic environment (Certo et al., 2006).
Multi view visualizers intend to enable users and
team developers with a more effective understanding
of the on going action, since it is expected to auto-
matically provide a set of distinct views, selected in
order to optimize their relevance with respect to the
understanding of the evolving simulation.
Selecting multiple views in virtual environments
is interesting to many other applications. As, for in-
stance, virtual museums or tourism in order to gener-
ate a best set of views over a set of relevant objects, or
to generate paths for virtual exploration. Another rel-
evant example is virtual cinematography, in order to
automatically position, select and move virtual cam-
eras.
The best set of views can be formulated as an op-
timization problem and it was chosen to conduct a
first set of experiments in order to test and validate the
previously described agent-based optimization frame-
work.
4.1 Problem Formulation
In the context of RoboCup Rescue, the best multi-
ple view problem can be informally stated as follows
(Moreira et al., 2006). In an urban rescue environ-
ment there are m visualization agents that can obtain
views over the scene. The problem is to find the set
of k views that better describes the simulation at each
moment. The visualization agents are controllable in
the sense that one can affect their viewing parameters.
In order to formulate this problem as an optimization
problem, we developed a simple model for estimating
the quality of a multi-view. The devised multiview
quality is a function of the visibility, relevance, re-
dundancy and eccentricity of the entities represented
in the set of selected views. Thus, the corresponding
optimization problem can be formalized as follows:
MAXIMIZE:Q(MV ) =
=
k
∑
j=1
n
∑
i=1
Vis(e
j
i
) · Red(e
i
|MV ) · (W
1
.Rel(e
i
) −W
2
.Ecc(e
j
i
))
E = {e
1
,... e
n
};V = {v
1
,... v
m
}
v
i
= f (
~
Pos
i
,
~
V D
i
,
~
VU P
i
,FoV
i
) i ∈ {1, ...,m}
MV = {sv
1
,...,sv
k
} where MV ⊂ V and sv
i
6= sv
j
∀i 6= j
In the given formulation, E denotes the set of n
entities that have relevance in the scene (buildings,
agents, etc) and V is the set of different views (equals
the number of agents/entities with viewing capabili-
ties). Each view is characterized by usual camera pa-
rameters, as position
~
Pos
i
, view direction
~
V D
i
, rela-
tive camera orientation
~
VUP
i
and field of view FoV
i
.
A multi-view, MV , comprises a set of k distinct
selected views (sv) from V .
The problem is to find the optimal MV set, with
appropriate view parameters, that better describes the
rescue scenario given a quality metric. We developed
a quality Q metric using the following criteria (note
that e
j
i
denotes the visual properties of an entity e
i
in
a image obtained by the view j). The parameters W 1
and W 2 denotes constants used to adjust the relative
weight of relevance and eccentricity.
Visibility: Vis(e
i
j
). This feature relates to the visibil-
ity of the relevant entities (e.g. the visible area).
Several factors contribute to an entity’s visibility
such as the distance to the viewpoint, size, relative
orientation, and also by how much partial occlu-
sion it suffers from other objects.
Relevance: Rel(e
i
). A measure of how relevant is
the entity for the purpose of the visualization.
For example, if tracking emergency situations, a
building on fire has a greater relevance than an un-
affected building. The intrinsic importance of an
object is also considered, e.g. hospitals, fireman
headquarters, schools have more relevance than
ordinary buildings.
Redundancy: Red(e
i
|MV ). It is expected that the
multiple set of views describe as much as possi-
ble distinct situations occurring during the simu-
lation. Thus, redundant views over the same enti-
ties are penalized.
Eccentricity: Ecc(e
i
j
). A measure on how distant to
the center of the image an object is displayed.
This criterion has a perceptual foundation based
on the observation that an user will focus atten-
tion to image centered entities rather than to those
in more peripherical regions.
An expected multi-view is shown in Figure 4. As
it is depicted, regions with very intense ongoing res-
cue operations are shown with greater detail. Simulta-
neously other ares covered by wider views configured
in order to get a more comprehensive picture of (an
area) of the rescue scenario.
Using larger sets of views may be also of interest,
for instance, for surveillance centers, allowing to have
a better coverage of the ongoing operations either in
coverage either in detail. The above general problem
formulation was used in the experimental setup with
some simplifications. Namely, it was made W 2 = 0,
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