Unformatted text preview: of each action are modeled as individual action sequences. Hence, we are able
to recognize each view by treating it as a distinct action
sequence and without having to incorporate information from the other view. 2.5 Discriminating similar actions For certain actions the head moves in a similar fashion.
For instance, when viewed from the front, during squatting, sitting down and bending down, the head moves
downward without much sideward deviation. Similarly,
during standing up, rising and getting up actions, the
head moves upward without much sideward deviation.
In order to distinguish these actions from one another,
we consider a discriminant number, whose value depends on how low the head goes in the performing of
these actions. During bending down, the head goes
much lower than in sitting down, and in sitting the
head goes lower than in squatting. Let g = maxyinput =maxytraining 12 In general, maxygettingup maxysitting maxysquatting 13 where g is the discriminant number obtained as a ratio
of the maximum y co-ordinate in the input sequnce to
the maximum y co-ordinate in the training sequences,
maxygettingup , maxysitting , maxysquatting are the
maximum values of the y co-ordinate of the head in the
getting up, sitting and squatting actions in the front
view. We compute ggettingup , gsitting , gsquatting , as
the discriminant numbers corresponding to the three
classes, namely getting up, sitting and squatting in
the front views, which are obtained using equation 12.
Thus whenever the system nds that the input action is one of the above three, it decides the most likely action by choosing that action which has the maximum
discriminant number. A similar process is invoked for
the rising from the squatting position, standing and
getting up actions. Other actions that are similar with
respect to the motion of the head can be distinguished
by considering the size of the head in successive frames.
Thus, a walking action in the frontal view, which is
similiar to the backwards bending action, can be distinguished by making use of the fact that the size of
the head increases over successive frames as the subject approaches the camera.
= max=min 14 where is the size of the head in one frame of the
action sequence and is the ratio of the maximum
and minimum sizes of the head taken over all frames
of that action sequence. If
, where is a prede ned threshold, then the computed probability for
the walking action in the front view is multiplied by a
weighting factor Wi . 3 Detection & Segmentation
The detection and segmentation of the head is central
to the recognition algorithm. We model our system
by estimating the centroid of the head in each frame.
Many human activity recognition algorithms depend
on e cient tracking of a moving body part 5, 9 . Similarly, in our case, the entire recognition algorithm is
based on reliably tracking the centroids of the head.
At this stage of the project we do the segmentation by
hand, isolating the head from the rest of the scene...
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- Spring '13
- Facial recognition system