The soccer playfield has been divided in 12 zones, 6 for each side (Figure 5.14). These zones have been
chosen
so that the change from one to the other indicates a change in the action shown, such as a defense action that changes into a counter-attack, or an attack that enters the goal box. Moreover it must be noted that typical camera views are associated with the selected zones. In some cases the zones can't be recognized from simple image analysis (e.g., zones near the center of the playfield), but the temporal analysis
solves
this problem.
The features used to recognize the playfield zones are playfield lines and the playfield shape. Figure 5.15 shows an example of extraction of playfield shape and playfield lines. From these features we calculate a five elements vector
composed
by the following elements:
-
playfield shape descriptor F: six different views of the playfield are recognized. They are shown in Figure 5.16 a);
Figure 5.16:
(a) Playfield shape descriptor F. (b) Playfield line orientation descriptor O.
-
playfield line orientation descriptor O: playfield lines are extracted, and their directions are quantized in 32 bins. These
bins
are grouped to calculate the number of horizontal and vertical lines, and the number of lines whose angle is greater or smaller than the specified line orientation descriptor, as shown in Figure 5.16 b);
-
playfield
size
descriptor R: this descriptor signals if the percentage of pixels of a frame, that belong to the playfield, is above a threshold;
-
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playfield corner position C: this descriptor may assume 4 values: absence of playfield corner in the image, or presence of one of three possible
positions
of the playfield corner that is farthest from the camera, as shown in Figure 5.17 a);
Figure 5.17:
(a) Playfield corner position descriptor C. (b) Midfield line descriptor M.
-
midfield line descriptor M: this descriptor is similar to the C descriptor, but it indicates the presence and type of the midfield line (Figure 5.17b).
Figure 5.15:
Original image, playfield shape and lines in soccer.
A Nave Bayes classifier has been used to classify each playfield zone
Zx.
This choice is motivated by the fact that:
i)
we are interested in confidence values for each zone, to handle views that are not easily
classified
;
ii)
some descriptors are not useful for some zones (e.g., the C descriptor is not useful for the midfield zone);
iii)
some zones are
mutually
exclusive and some are not, thus it is not possible to define them as different values of a single variable. In Figure 5.18 the
classifiers
for Z1 and Z6 are shown. It must be noted that in some cases some values of the descriptors
correspond
to a single value of the variable of the classifier, e.g.,:
Figure 5.18:
Nave Bayes networks— Z1 and Z6 zone classifiers.
Playfield zone classification is performed through the following steps: playfield lines and shape are extracted from the image, descriptors are then calculated and the observation values of the classifier
variables
are selected. The classifier with the highest confidence value (if above a threshold) is selected.
7.
Camera Motion Analysis
As noted in section 5, camera parameters are strongly
related
to ball movement. Pan, tilt and zoom values are calculated for each shot, using techniques shown in section 3. The curves of these values are filtered and quantized, using 5 levels for pan, 3 for tilt and zoom. Analysis of the
videos
has shown that conditions such as a flying ball rather than a change of direction can be
observed
from camera motion parameters. Through heuristic analysis of these values, three descriptors of
lo
w, m
edium
and
high
ball motion have been derived. Figure 5.19 shows a typical shot action: at time
t
start
a player kicks the ball toward the goal post. Acceleration and deceleration (at time
t
OK
) can be identified. Often zoom on the goal post can be observed.
Figure 5.19:
Typical shot action— at time t
start
the ball is kicked toward the goal post. Symbols on the right identify low, medium and high motion.