![](bg6.png)
where the first condition is based on the prior
probability to make sure the sample is mostly from
background class. The second and third conditions
pick out the samples which can’t be separated from
background class in the orientation-based LDA
space and thus the system need colour-based LDA
space to deal with these samples, i.e. to reduce the
likelihood value in the colour-based LDA space and
thus to reduce the overall likelihood probability for
rescuing from the confusion case. Similarly, the new
data set
o
t
S
used for updating orientation-based
LDA model can be defined as
⎭
⎬
⎫
⎩
⎨
⎧
>∩<∩<=
== 221
)|()|()(|
γγγ
i
tt
LDA
colf
i
tt
LDA
orif
i
t
i
t
c
t
xzpxzpxpcS
(21)
Finally, for each feature type
f, the mean vectors
f
m
and
f
m
for the object and background classes
are updated by the SKL (sequential Karhunen-
Loeve) algorithm (Levy et al., 2000) using the new
data sets
c
t
S (or
o
t
S ) and the new LDA projection
matrix Φ (or Ψ) is then calculated using the
Incremental Fisher Linear Discriminant Model as in
(Lin et al., 2004).
4 EXPEREIMENTAL RESULTS
The effectiveness of the proposed tracking system
was evaluated using three different tracking
sequences, namely two noisy real-world video
sequence (H1, H2) captured from YouTube.com, a
head target sequences a (H3) taken from a
benchmark data set (
Birchfield, 1998). Table 2
summarizes the variation property of each test
sequence. The tracking system was initialized by
using
N
p
=150 object feature vectors and N
n
=400
background feature vectors to create LDA models Φ
and Ψ, respectively. In the tracking process, the
particle filter uses
N
s
=150 samples (Eq. (6)) and the
5-dimensional vector
B (Eq. (8)) is a multivariate
Gaussian random variable with zero mean and the
standard deviation of 10 pixels, 10 pixels, 8 pixels, 8
pixels, and 5 degrees, respectively. Note that the
thresholds used in building the validation sets and
evaluation sets were set experimentally a
s γ
1
=γ
3
=0.8,
γ
2
=γ
4
=0.7.
The accuracy of the tracking results obtained
from the AMF-PFI system was quantified using the
tracking error
)(te
, defined as the discrepancy
between the estimated target object state (estimated
target window) at time
t and the manually labelled
ground-truth state (ground-truth target window), i.e.
Table 2: Description of the test sequences.
Seq. Property
H1 Noisy, low-quality, real-world video
H2 Clutter, noisy, low-quality, real-world video
H3
Out-of-plane rotations, scale changes, clutter,
occlusions
)()(
)(2
1)(
tAtA
tO
te
eg
+
−=
(22)
where
∑
∈
−
+
−
=
TruePixelsyx
g
gi
g
gi
ii
h
vy
w
ux
tO
),(
22
)
2/
()
2/
()(
(23)
where
)(tO
sums the importance of the true positive
pixels utilizing distance as an importance measure.
),(
ii
yx is the x- and y- coordinate of the true
positive pixel,
w
g
, h
g
, (u
g
, v
g
) are the width, height,
and centre of the ground-truth target window,
respectively.
)(tA
g
and )(tA
e
are normalized
terms, which sums up the importance of all pixels
within the ground-truth and within the estimated
target, respectively.
The performance of the proposed system (names
as AMF-PFI) was compared with that of two other
systems, namely a system without adaptive feature
confidence value, denoted MF-PFI, and a system
without an incremental LDA model, denoted as
AMF-PF. Note that each test sequence was tested 10
times for each framework (Maggio et al., 2007).
Table 3 summarizes the mean and standard deviation
of the error metric for each of the three frameworks
when applied to the three test sequences. The results
confirm that the proposed system achieves a better
tracking performance than either the MF-PFI or
AMF-PF systems. The performance improvement is
particularly apparent for test sequences H1 and H2,
in which the targets exhibit significant appearance
changes over the course of the tracking sequence.
Fig. 2 shows the tracking results obtained by the
proposed system for test sequences H1 and H2. The
sequence H1 is simple case that the colour feature of
target object is much different from background but
in H2 the target object is cluttered by background. In
both sequences, the target object has out-of-plane
rotation. The results confirm the robustness of the
proposed system toward out-of-plane rotations. Fig.
3 shows the object tracking results (first row) for the
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