performance of a videobased ITS front end depends
on the joint operation of two operations: change
detection and background update.
All imaging devices introduce interframe
variations due to sensor noise. Thus, direct frame
differencing fails to provide accurate results as a
change detection method. In general, change
detection demands the utilization of methods which
separate optimally content changes from noiselevel
fluctuations.
In addition, although a static background is
assumed, its appearance exhibits intensity variations
due to gradual changes in lighting conditions. In
highway scenes, an apparent factor that causes
alterations in the background scene is the influence
of daylight variations. This fact dictates the use of a
background update method, which adapts to the
present lighting conditions, without incorporating
occlusions to the background model.
The present paper analyzes the operation of a
videobased ITS front end which performs feature
extraction in the image domain. In detail, the paper
is structured as follows. In Section 2, a blockbased
clustering procedure, which performs noise model
estimation, is presented. In Section 3, a statistic
change detection method which takes into account
the noise model information is analyzed. In Section
4, an algorithm which performs background model
adaptation to gradual illumination variations is
presented. In the end, a short discussion analyzes
further development plans on the architecture of the
proposed system.
2 NOISE MODEL ESTIMATION
Feature extraction in traffic surveillance is feasible
through the application of change detection. A
highway frame, which is free of occlusions, can be
selected as reference. The change detection method
is responsible for the detection of occlusions on the
surveyed scene. This is performed through
comparison of subsequent frames against the
reference frame. The operator can restrict the feature
extraction process by specifying a region of interest
or even multiple regions of interest. The latter case
applies when the operator is interested in the
inspection of each highway lane individually.
Since change detection requires the existence of
a reference frame, there is the need to ensure its
availability. The background frame may either be
selected manually or be created by applying
temporal median to a training video sequence
(Massey, 1996). In the latter case, manual
verification is suggested, in order to ensure that no
static occlusions have been erroneously incorporated
to the background model.
An algorithm which performs change detection
needs to cope with the noise effect. Noise is an
inherent characteristic which is present in every
image acquisition sensor and introduces interframe
variations even in “unchanged” scenes. This fact
precludes direct differencing and requires the use of
a technique that separates content changes from
noiselevel fluctuations. Obviously, content changes
and noise variations are not always separable. Thus,
change detection algorithms focus on the estimation
of the optimum “threshold of perception” and the
formulation of an optimized classification rule.
Change detection methods encountered in the
literature (Radke, 2005) are based on statistic
criteria, in order to decide whether a pixel or a block
of pixels corresponds to a changed or an unchanged
region. A statistic approach proposed in (Aach,
1993), (Cavallaro, 2001) applies a statistic
significance test over a rectangular window which is
centred in the pixel of interest. The approach
assumes a Gaussian noise model and introduces a
2
χ
probability distribution function in the
formulation of the classification rule. However, the
utilized significance test relies on prior knowledge
of the noise model and therefore requires the
availability of statistic noise information in order to
operate reliably.
The method which is analyzed in the present
paper aims to estimate the noise model  that is, the
noise mean value
n
m and standard deviation
n

from the image blocks of the absolute difference. Let
ij
m
and
ij
denote the observed mean value and
standard deviation in each block
ij
B of the absolute
difference. In order to achieve an accurate noise
model approximation, it is sufficient to restrict the
noisemodel calculations to the block subset which
exhibits noiselevel fluctuations. Therefore, the goal
of the proposed procedure is to group blocks of the
absolute difference to clusters, according to their
statistic similarity (Alexandropoulos, 2005).
In order to perform the clustering operation, each
cluster
k
C is described by two statistic parameters:
the averaged mean value
k
C
m
and the averaged
standard deviation of its member blocks. Thus, for a
cluster with
k
N member blocks, we define:
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