Object Detection Using Haar-like Features
The object detector described in [
Viola01]
and [
Lein02]
is based on Haar classifiers. Each classifier uses
k
rectangular
areas (Haar features) to make decision if the region of the image looks like
the predefined image or not.
Figure
“Types of Haar Features”
shows
different types of Haar features.
Types of Haar Features
In the Intel IPP Haar features are represented using
IppRect
structure.
Figure
“Representing Haar Features”
shows
how it can be done for common and tilted features.
Representing Haar Features
When the classifier
K
t
is applied to the pixel (i
,
j
) of the
image
A
, it yields
the value
val1(t)
if
and
val2(t)
otherwise.
Here
is a feature weight,
w
l
norm(i, j)
is
the norm factor (generally the standard deviation on the rectangle containing
all features),
threshold(t)
,
val1(t)
and
val2(t)
are
parameters of the classifier. For fast computation the integral representation
of an image is used. Haar classifiers are organized in sequences called
stages
(classification stages
). The
stage value is the sum of its classifier values. During feature detecting
stages are consequently applied to the region of the image until the stage
value becomes less than the threshold value or all stages are passed.