![](bg2.png)
1990), or Gabor filters (Jain et al., 1997) to create a
reference background frame. It is used to segment the
foreground objects by subtracting the reference frame
from the current frame of the input sequence. How-
ever, these methods are not particularly effective for
sequences with high-frequency background changes.
Slightly better results were reported for techniques
that rely on a Gaussian-based statistical model whose
parameters are recursively updated in order to fol-
low gradual background changes within the video se-
quence(Boult et al., 1999). More recently, this model
was significantly improved by employing a Mixture
of Gaussians (MoG), where the values of the pixels
from background objects are described by multiple
Gaussian distributions(Stauffer and Grimson, 2000).
This model was considered promising since it showed
good foreground object segmentation results for many
outdoor sequences. However, weaker results were
reported (Li et al., 2004) for video sequences con-
taining non-periodical background changes (e.g. due
to waves and water surface illumination, cloud shad-
ows, and similar phenomena). These models are para-
metric in the sense that they incorporate underlying
assumptions about the probability density functions
(PDFs) they are trying to estimate.
In 2003, Li et al. proposed a method for fore-
ground object detection employing a Bayes decision
framework (Li et al., 2004). The method has shown
promising experimental object segmentation results
for sequences containing complex variations and non-
periodical movements in the background. In addi-
tion to the generic nature of the algorithm where
no a priori assumptions about the scene are neces-
sary, the authors claim that their algorithm can han-
dle a throughput of about 15 fps for CIF video res-
olution. The approach is specific in the fact that it
uses a statistical model of for the changes between
the current frame and the reference background image
maintained by applying an Infinite Impulse Response
(IIR) filter to the sequence. A Bayesian classifier is
then used to classify the changes, detected through
frame differencing between the current frame and the
reference frame, as pertinent to background objects
or foreground objects. The statistical model is non-
parametric since it does not impose any specific shape
to the PDFs learned. The model is general in terms of
features extracted from the sequence and they experi-
mented with the use of different features. The results
of these experiments are reported in (Li et al., 2004).
Recently the approach of Li et al. has been adopted
and extended to create a part of a surveillance sys-
tem intended for maritime environments (Socek et al.,
2005).
While the use of Bayesian models as bases for
background subtraction is not new, it has been limited
by the fact that they are general in the sense that they
impose no constraints on the shape of the estimated
probability density function. This typically makes
them more computationally expensive than most of
their more restrictive counterparts (e.g.(Boult et al.,
1999)(Stauffer and Grimson, 2000)). However, mov-
ing away from the particle estimator systems used
typically to estimate probability density functions in
the Bayesian models (Li et al., 2004) to neural net-
works, it is possible to make them suitable for parallel
execution and increase their effectiveness.
Classical Probabilistic Neural Network
(PNN)(Specht, 1990) architecture has been used
by researchers to improve the object segmen-
tation(Doulamis et al., 2003) and perform the
classification of segmented objects (Azimi-Sadjadi
et al., 2001). In both solutions the neural network is a
supervised learning classifier guided by a a different
supervisor classifier algorithm.
In (Doulamis et al., 2003) authors present an unsu-
pervised video object (VO) segmentation and tracking
algorithm based on an adaptive neural-network archi-
tecture. Object tracking is handled as a classification
problem and implemented through an adaptive net-
work classifier, which, however, relies on the results
of the initial video object segmentation module to ad-
just itself to the variations of the sequence. The neural
network is but a part of the background segmentation
algorithm. Hence, the whole system does not posses
inherent parallelism of the PNN. As such, the system
is not suitable to serve as basis of an efficient hard-
ware implementation.
An approach employing a PNN classifier in a time
varying environment is proposed in (Azimi-Sadjadi
et al., 2001). A PNN was used to classify clouds
based on their spectral and temperature features in the
visible and infrared GOES 8 (Geostationary Opera-
tional Environmental Satellite) imagery data. A tem-
poral updating approach for the PNN was developed
to increase the classification accuracy by accounting
for the temporal changes in the data. The network it-
self is a supervised leaner and is updated every time
a new frame is processed. As in the approach of
(Doulamis et al., 2003), the PNN is a submodule of
the system, and its parallelism can only partially be
exploited in a hardware implementation.
3 BACKGROUND MODELING
NEURAL NETWORK (BNN)
The proposed background modeling and subtraction
approach relies on a novel adaptive neural network.
The architecture employs an adapted General Regres-
sion Neural Network (GRNN) (Specht, 1991) com-
ponent, to serve as an estimator of the PDF of cer-
tain features belonging to background. GRNNs, typi-
cally used as Bayesian classifiers, are supervised clas-
A NEURAL NETWORK APPROACH TO BAYESIAN BACKGROUND MODELING FOR VIDEO OBJECT
SEGMENTATION
475