International Core Journal of Engineering 2020-26 | Page 78

different weights. If the computation cost is expected to be
reduced to the maximum extent, the quantization step will be
reduced in an exponential manner
Camera
of
The usage of dynamic quantization step leads to a
problem that we need to match the feature vectors with
different dimensions in the previous and current frames. In
order to match the features with different dimensions, the
DTW algorithm [12] has been introduced. DTW is a
common used model matching method, it maps the
to-be-matched feature T nonlinearly to the same scale as the
reference feature R through a specific function. Assume that
the length of the feature to be matched is N, the length of the
reference feature is M, the dimensions of the feature to be
matched and the reference feature are respectively marked
on the horizontal axis and the vertical axis of the coordinate
system, thereby, forming a grid in the coordinate system.
Any intersection point (xi, yj) in such grid stands for the
intersection of the two features, and the cost function of the
intersection point is D[i, j], (i=1…N, j=1…M). Assume that
all the lattice points that the path passes by are (x1, y1),…,
(xi, yi),…, (xN, yM), then we have:
C
B
A
B, C
A
Fig. 1. Demonstration of the spatial position and video image of the target
moving along the depth of field.
Fig. 1 shows the demonstration of positional
relationships and video images in the scenarios where the
person is moving along the depth of field. It can be clearly
seen that the moving object’s discriminability and the
occupied image’s area are greatly reduced in position A as
compared to that in the position B and C, which inspires us
to consider whether the targets can be expressed in a simpler
way.
The key of the proposed algorithm is to use dynamic
quantization step in the generation of the models. The
initialization process of the algorithm is the same as that of
traditional meanshift tracking algorithm. After initialization,
the candidate model is firstly extracted in the candidate area,
a new target position is determined through meanshift
iteration and the tracking area is updated. Note that the focus
of this paper is to dynamically adjust the quantization step,
the target-size adaptation method is simply introduced from
[11]. The quantization step will be adjusted once the size of
the target changes drastically
s i  1
(1  C ) s i Ÿ k i  1
(1  C ' ) k i
D ( i , j )
­ D ( i , j  1) ½
°
°
® D ( i  1, j ) ¾  d ( x i , y j )
° D ( i  1, j  1) °
¯
¿
(2)
Experimental results With the help of the local cost
function d(xi, yj), and considering the three possible points
D(i,j-1),D(i-1,j),D(i-1,j-1) before the current point, the path
with the smallest cumulative cost function can be found by
backtracking from the point (xN, yM) to the point (1,1),
which represents the distance between the two sequences.
III. E XPERIMENTAL R ESULTS
(1)
In order to verify the effectiveness of the proposed
method, the tracking experiments were conducted in the
environment of VS2010. The hardware environment of the
computer platform was Intel quad-core 3.1GHz CPU, 8G
memory, 1TB hard disk and the operating system was WIN7.
Experimental results were compared with those from the
traditional meanshift in different environments. The
quantization step adaptively changing of the proposed
algorithm and the difference of the operating efficiency
between the proposed algorithm and the traditional algorithm
were mainly investigated. The frame number, target sizes
and quantization steps were given below each resulting
image.
where C and C’ are weights for updating the size of the
tracking window and the quantization step; k is the current
quantization step. s is the scale parameter to control the
linear adjustment of the target size. When the target size
zooms out or zooms in to a certain extent, the quantization
steps of color histograms are adjusted. Eq. (1) indicates that
if the target scale parameter become C times of the original
value, the current quantization step will be adjusted by C’
whose specific values depends on the scene-depth and the
actual geometry of the tracked object. The height and width
of the target can be adjusted using the same C or with
Fig. 2. Comparison of Tracking results in experiment 1
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