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K.a.D., 2004) proposed a method which employs
Active Contour Models (ACM) to detect moving
objects and neural networks to classify the shapes
obtained by ACM as either 'human' or 'non-human'.
This technique is in contrast with other methods of
shape description which relies on having a one to
Figure 1: A sample input frame and the output results of
different steps of algorithm.
one correspondence between landmark points on the
shape model and the current contour, and still suffers
from occlusion. A.Koschan (Koschan,S.K.K, 2002)
uses Active Shape Models (ASM) as human shaped
objects detector; in addition the colour information
contributes to the solution of occlusions.
Nevertheless, the tracking of a person becomes
rather difficult if the image sequence contains
several moving persons with similar shape and the
task may fail if the person is partially occluded.
These approaches seem to fail in situations where
people walk next to each other and/or occlude one
another; however, Zhao and Nevatia (Zhao and
Nevatia, 2001) employ Markov chain Monte Carlo
technique as a method for finding the omega pattern,
formed by the head and shoulders, which can
overcome the occlusion problem but the complexity
of MCMC method is an obstacle against working in
a real time manner.
The second category uses the image processing
statistical methods instead of detecting people for
the counting task. These methods apply different
features of objects which can be the blob size
(Masoud and Papanikolopoulos, 2001) , (kong, Gray
and Hai, 2006), (Aik and Zainuddin, 2009), the
Fractal Dimension (Rahmalan, Nixon and Carter,
2006), the bounding box (Masoud and
Papanikopoulos, 2001), and also edge density (kong,
Gray and Hai, 2006), (Villamizar and Sanfeliu,
2009). These methods can be employed for real time
application but they have lower accuracy than the
methods in the first category.
In this paper we explorer an alternative technique
based on a novel integration of multiple hypotheses
for the detecting and tracking of human head-
shoulder regions in order to count them in entrance
gates which brings this method into the first
category. In addition, for the crowd situations, we
employ an estimation method which uses spatial
features i.e. blob size, edge density and orientation,
which places this component into the second one.
The algorithm does not produce unique trajectories,
but we show that after a one-time estimation of a
systematic correction factor based on manually
labelled ground truth data, accuracies up to 99 % can
be achieved for real-world scenarios. A snapshot of
our results is shown in Fig. 1.
The outline of this paper is as follows. Section 2
first gives a brief description of the system, in
addition reviews the different algorithms and their
role in this approach. We illustrate a detailed
analysis of our real-world tests of the system in
section 3. And finally we conclude the paper in
Section 4.
2 SYSTEM DESCRIPTION
Most of the previous works, assume that pedestrians,
regardless of their clothes and hairstyles, display a
typical Ω-like shape which is formed by their heads
and shoulders. But in some areas like the sacred
places where religious people wear special clothes,
other potential head candidates can come into
account, namely in an Islamic place most of women
wear a long black veil and clergymen wear a special
hat which can result in different shape of heads, like
O or Λ. Based on this fact, beside employing the Ω-
like shapes extraction for finding heads, we also
notice O-like and Λ-like shapes. An efficient feature
vector for demonstrating the head shape features
also have been developed.
Lots of accurate methods like Zhao et al. (Zhao
and Nevatia, 2001) suffer from time complexity and
do not fit into real time constraints. Since most of
counting applications are needed to be real time, we
apply a further pre-processing step and also a
trained PCA in order to find heads while reducing
the processing time.
In order to find heads, first a foreground map,
based on Gaussian Mixture Models (GMM)
(Stauffer and Grimson, 1999) is used to segment the
objects from the background which can overcome
the known problems of adaptive background models.
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