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4.3. Automatic Interest Point Detection
S(t) =IE(ωpSp(t)+ωsSs(t)+ωoSo(t)+ωpeSpe(t)) (4.3)
where IE is the impact factor of E. IE can be used to give more impact to certain entities.
For example, a user may want objects to be more important than characters. Once the best
overall scores Smax(t) (best S(t)) have been computed, we define the attention threshold A
which determines the minimum score value for an attention behavior to be activated. This
cannot be defined as an absolute threshold since the overall score values can greatly vary.
For example, overall scores tend to be much higher in dense areas than in sparser ones. We
thus compute A as the (100-a) th percentile of Smax(t). a being randomly generated by the
application and different for each character. The character will therefore only pay attention
to interest points a percent of the time. The corresponding interest points will be the ones
that have a higher score than A. This is depicted in Figure 4.1.
Our method automatically generates attention shifts since we calculate the interest points
at each frame and for each character. However, if the interest point stays the same for a very
long time, this generates unlikely behaviors. For example, if two characters are walking side
by side, their respective scores for each other may be very high due to their proximity. In this
case, they will keep on staring at each other, producing unrealistic attention behaviors. We
therefore define a threshold duration dl. If an interest point lasts for more than dl, the entity
of next highest interest is chosen as new interest point (as long as its score remains above the
attention threshold). We arbitrarily set dl to a maximum value of 4 seconds for the attention
behavior not to last indefinitely. This also simulates interesting emergent behaviors. In the
example given above, two characters walking side by side will oscillate between looking at
each other and looking at another entity or back in front of them. They will thus give the
impression of characters talking together.
The following sections explain the different elementary functions we implemented in
more detail. All algebraic notations in these sections are represented in Figure 4.2.
4.3.1 Proximity
The proximity parameter evaluates the distance between a character C and all other entities
E. Givendce(t), the distance between C and E at time t, and α(t) the orientation of E in
C’s field of view at time t, our proximity score is computed as:
Sp(t) =exp( −(0.5(dm − dce(t)) + ( dm
2
2
− 1))2
) (4.4)
where dm is the maximal distance value beyond which C will stop looking. This value may
be modified by the user; he/she may wish to set dm to a small value for narrow and densely
populated areas. Conversely, he/she may wish to set it to a higher value for large open spaces.
Our computation allows for entities situated 2 − 3 meters away from the character to obtain
the highest proximity scores. Indeed, we believe that entities closer than this will already
have been attended to and thus should lose their interest potential.
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