PERFORMANCE OF ADAPTIVE TRACKING ALGORITHMS
Janeth Cruz, Leopoldo Altamirano and Josu
´
e Pedroza
National Institute of Astrophysics, Optics and Electronics
Enrique Erro No. 1, Sta. Mar
´
ıa Tonantzintla, Puebla, M
´
exico
Keywords:
Adaptive tracking algorithms, Estimation filters, Performance evaluation.
Abstract:
This paper compares the performance of adaptive trackers based on multiple algorithms. The aim of using
multiple algorithms is to increase the robustness of the trackers under varying conditions. We perform two es-
timation algorithms UKF and IMM to measure the performance of tracking on outdoor scenes with occlusions.
The purpose of this paper is to measure and evaluate tracker reliability for be able to determine the position of
a target. The performance is evaluated using metrics related to truth track. We give a positional evaluation and
statistics values of the performance of visual tracking systems, which adapt to changing environments.
1 INTRODUCTION
Automatic tracking algorithms are employed in many
industrial and military applications. In practice, auto-
matic target tracking systems need to operate around
the dynamic environments and require high accurate
determination of target position, velocity, acceleration
and other parameters to increase the detection proba-
bility and reduce false alarms and missed targets pro-
babilities.
There are many algorithms for tracking, such as,
correlation trackers (Kishore and Rao, 2001; Ronda
and Shue, 2000) that perform well with structured
targets, even in highly cluttered background con-
ditions. However correlation walk-off and false
peak problems are critical in these trackers. The
centroid-based tracking algorithms are also used in
surveillance systems (Jae-Soo Cho and Park, 2000).
These trackers are especially susceptible to changes
in object shape and orientation between successive
images. Another approach used is edge tracking,
which presents drawbacks in low contrast images.
In most cases tracking algorithms fail due to low
contrast, noise, scale and illumination changes. Se-
veral approaches have been developed to improve the
tracking of a moving target based on multiple trackers
(Ronda and Shue, 2000; Kishore and Rao, 2001;
Tao Yang and Li, 2005). However, these proposals
have not solved the occlusion problem that an object
presents in its trajectory. For the solution is necessary
to estimate the target position, by means of motion
models and estimation filters.
The aim of these approaches is to increase the
adaptability of the tracking to varying conditions; the
adaptability degree of the algorithms can be obtained
with the measurement of the uncertainty. Therefore,
several techniques have been proposed to measure
and compare different tracking algorithms.
Some papers evaluate the performance of full
tracking algorithms through occlusions. In (J. Black
and P., 2003) a methodology for evaluating the perfor-
mance of tracking systems is presented. They test the
performance of a tracking algorithm that employs a
partial-observation tracking model for occlusion rea-
soning. Needham and Boyle (Needham and Boyle,
2003) compare two tracking systems for different ob-
jects by using a set of metrics for positional eva-
luation. The work in (L. M. Brown and Lu, 2005)
presents a comparison of two background subtraction
algorithms with indoor/outdoor scenes. The number
of false negatives and false positives of each algo-
rithm is obtained for comparison. Lefebvre et. al.
(Tine Lefebvre and Shutter, 2004) compares the qua-
lity of the estimates of the common Kalman filter va-
riants for nonlinear systems. This quality is expressed
in terms of consistency and information content. Hall,
et. al. (D. Hall and Crowley, 2005) present and eva-
luate five adaptive background subtraction techniques
with background models of different complexity.
In this paper, we compare three trackers based on
229
Cruz J., Altamirano L. and Pedroza J. (2006).
PERFORMANCE OF ADAPTIVE TRACKING ALGORITHMS.
In Proceedings of the First International Conference on Computer Vision Theory and Applications, pages 229-236
DOI: 10.5220/0001377702290236
Copyright
c
 SciTePress

multiple algorithms in order to maintain the target
trajectory. To track objects through occlusions we
used UKF and IMM filters. The trackers are eva-
luated on the same benchmark data set which allows
a more objective comparison. The metrics for com-
paring trackers evaluate the positions estimated and
the detection’s reliability.
The paper is structured as follows: Section 2
presents a brief review of tracking algorithms. Sec-
tion 3 describes the motion models and estimation
filters used in order to increase the robustness of the
algorithms. In section 4 we describe the process used
to obtain the truth track and evaluation performance
metrics applied. Section 5 describes the data set used
and shows the performance results on data set. Fi-
nally, we give our conclusion of the comparative ana-
lysis in section 6.
2 TRACKING ALGORITHMS
Tracking algorithms are divided mainly in two main
categories: region trackers and edge trackers. For
a region tracker, a region of the image is selected
as search pattern and one similarity measure is used
to decide on the best matching region in the next
image. These algorithms fails in the case of changes
in in object size, illumination changes, and surface re-
flectance. Their challenges are to make possible a gra-
dual adaptability to different conditions in the images
and to avoid to slowly drifting from the tracked region
into the background.
On the other hand, edge trackers follow edges pro-
duced by changes in the reflected light (variation in
colour or illumination). However, an edge detection
algorithm requires smoothing and differentiation of
the image. Differentiation is an ill-conditioned pro-
blem and smoothing results in a loss of information.
Therefore, it is difficult to design a general edge de-
tection algorithm which performs well in many con-
texts.
2.1 Correlation
Correlation is performed by overlapping a correlation
window holding a reference image at the location of
each pixel in a search region from the current frame.
A correlation metric is used to define the best mat-
ching of the correlation window in the current image.
The portion of image most similar is registered as a
new reference image for correlation at the next frame.
The correlation process might give an incorrect
registration because low contrast of the reference
pattern, sensor noise, occlusion, etc. In applications,
where the reference has to be updated in each frame
to reduce the effect of magnification, occlusion, etc,
a single incorrect registration will lead to false track.
For these applications, it is necessary confidence mea-
sures in order to validate a correct registration. Ronda
et al. in (Ronda and Shue, 2000) define a confidence
measure, to prevent false updating of the reference
pattern. They present a mean-subtracted fully nor-
malized correlation algorithm (MSFNCA) that is an
improvement to simple correlation. The objective of
this approach is the robustness to variations in image
intensity. The confidence measure is the following:
MSF NCA(i, j) =
PP

(R(l, m) −
¯
R)
(S
ij
(l, m) −
¯
S
i,j
)


PP
(R(l, m) −
¯
R)
2
PP
(S
ij
(l, m) −
¯
S
ij
)
2

1
2
=
PP
[R(l, m)S
ij
(l, m)−
M
2
¯
S
i,j
¯
R


PP
R
2
(l, m) − M
2
¯
R
2


PP
S
2
ij
(l, m) − M
2
¯
S
2
ij

(1)
where R is the reference image, of size M × M . And
the M ×M window of the search image at pixel (i, j)
is denoted by S
ij
.
The target might drift out of the reference image
due to the discrete pixel size and the target motion,
even using the MSFNCA confidence measure. This
drift has to be corrected locally using others detection
algorithms.
2.2 Centroid Tracking
Centroid tracker is one of the most common algo-
rithms used (Jae-Soo Cho and Park, 2000), which
determines a target aim point by computing the in-
tensity or geometric centroid of the target object
based on target segmentation process. The Figure 1
shows the target’s centroid. The performance of cen-
troid tracker is largely dependent on the segmentation
algorithm which needs to extract the moving tar-
get even in complex background conditions. There
are many algorithms for image segmentation (Sezgin
and Sankur, 2004), such as histogram-based meth-
ods, clustering-based methods and local methods that
adapt the threshold value to local image characteris-
tics. In this work, we define a threshold for each se-
quence.
2.3 Edge Tracking
Edge detection concerns the localization of varia-
tions of the grey level image and the identifica-
tion of the physical phenomena that originated them
(Djemel Ziou, 1998). To detect the target edges was
implemented the SUSAN algorithm, which performs:
VISAPP 2006 - MOTION, TRACKING AND STEREO VISION
230

Figure 1: Centroid of a target.
edge and corner detection and structure preserving
noise reduction.
This algorithm is a type of neighborhood voting
method to enhance the edges and corners of 2D
images. The speed and localization are quite good.
The SUSAN algorithm is implemented using digital
approximation of circular masks (Smith and Brady,
1997). If the brightness of each pixel within a mask
is compared with the brightness of that nucleus of
the mask, then an area of the mask can be defined
which has the same brightness as the nucleus. In this
approach no image derivatives are used and no noise
reduction is needed. However, the method produces
incorrect results with noisy images.
2.4 Adaptive Trackers
In tracking applications where a target changes shape
and size frequently, conventional tracking algorithms
as correlation are also applied. However, if a false re-
gistration occur and is not detected, the system could
fail due to the reference pattern is updated in each
frame.
False registration problem occurs when the refe-
rence pattern drifts away from target area. In order
to solve this problem, some works use correlation
algorithms as MSFNCA with a confidence measure
which ensures a better registration than a simple cor-
relation. This correlation performs well in many sit-
uations. However the drift problem and incorrect re-
gistration still persist.
In these cases, where the information of one algo-
rithm is not sufficient for maintaining the target tra-
jectory, we can apply multiple tracking algorithms in
order to increase the reliability of the system.
Figure 2 shows the three algorithms used in this
paper. Tracker 1 (Correlation + Centroid) performs
a correlation tracking and if coefficient correlation is
greater than a threshold then centroid algorithm is
performed. We set the threshold of the confidence
measure to 0.3. Tracker 2 (Correlation + Centroid +
Edges) uses three trackers to maintain the truth tra-
jectory, this tracker integrates edge detection algo-
rithm to increase the performance. Tracker 3 (Corre-
Figure 2: Tracking with multiple algorithms.
lation with gradual updating) performs a correlation
algorithm where the reference image is updated at the
next frame, replacing each pixel (i, j) of the reference
image by using equation 2.
R
ij
= (t + 1) = R
ij
(t)α + (1 − α)S
ij
(t) (2)
where α was set to 0.01.
3 ROBUST TRACKING
Occlusion is one of the problems for maintaining the
trajectories of the targets. Target features are lost
during an occlusion. Therefore, in the absence of in-
formation about the target, the state prediction can be
useful. In this paper, robust tracking is achieved by an
Unscented Kalman filter (UKF) and Interacting Mul-
tiple Model filter (IMM).
For predicting object state is necessary a motion
model, which represents the kinematic of the ob-
ject. For this comparative analysis we are using two
mathematical models (Li, 2000): acceleration cons-
tant model (AC) and velocity constant model (VC).
The state space for the models is of four dimen-
sions, defined by x− and y−position and x− and
y−velocity.
3.1 Estimation Filters
The Unscented Kalman filter (UKF) is a minimum
mean squared error (MMSE) state estimator for a
nonlinear system (Julier and Uhlmann, 1997). To esti-
mate the effect of the nonlinear and non-Gaussian mo-
dels, this filter uses a deterministic sample based ap-
proximation. The basic component of this filter is the
unscented transformation which uses a set of appro-
priately chosen weighted points to parameterize the
means and covariances of probability distributions.
Moreover, the IMM approach estimates a target
state when the target maneuver is unsure and it is sub-
ject to changes (Bar-Shalom and Blair, 2000). The
PERFORMANCE OF ADAPTIVE TRACKING ALGORITHMS
231

Figure 3: IMM filter.
IMM includes a finite set M of different maneuver
models that cover the possible maneuver spectrum.
Each model m ∈ M describes a different dynamical
system.
One cycle of IMM estimator includes the following
steps (Figure 3): interaction/mixing, filter M
r
, update
of mode and mixing probabilities, and combination
of state estimate and covariance. The IMM mode-set
was designed with two linear Kalman filters and two
motion models (AC and VC).
4 CONFIDENCE MEASURES
In order to achieve reliable performance, it is ne-
cessary to have confidence measures of the results.
There are many error metrics for tracking. They can
be divided into two categories: (1) statistical methods
that compare the measurement data obtained from the
tracker with the truth data, and (2) accuracy measures.
This analysis lies in comparing the tracking al-
gorithms, their applicability on different scenes and
the error obtained from prediction process. First, we
compare the trajectories using positional metrics, and
then we assure the performance of the robust trackers
with accuracy measures.
4.1 Truth Track
In order to identify trajectories of poor quality for the
tracking, the truth tracks are checked for consistency
with respect to path coherence (Xu and Ellis, 2002;
J. Black and P., 2003). The objective of applying this
measure is to consider the complexity of the target
motion in the scenes.
The path coherence is given by:
ε
pc
=
1
N − 2
N−1
k=2
w
1
1 −
x
k−1
x
k
·x
k
x
k+1
kx
k−1
x
k
kkx
k
x
k+1
k
+
w
2
1 −
2
√
kx
k−1
x
k
kkx
k
x
k+1
k
kx
k−1
x
k
k+kx
k
x
k+1
k
(3)
where N is the number of frames, x
k−1
x
k
is the
vector that represent the positional shift of the tracked
target between k and k − 1. w
1
and w
2
are weighting
factors that define the contribution of the components
(direction and speed) of the measure. These weights
were set to 0.5.
4.2 Comparison of Trajectories
For comparing trajectories, we apply some metrics
used in (Tine Lefebvre and Shutter, 2004). One of
the measures consists to know which is the displace-
ment and distance between the target trajectory from
the tracker T
E
and the truth trajectory T
T
with posi-
tions (x
i
, y
i
) and (p
i
, q
i
) respectively. The displace-
ment d
i
between trajectories at time i is calculated
using equation 4.
d
i
= (p
i
, q
i
) − (x
i
, y
i
) = (p
i
− x
i
, q
i
− y
i
) (4)
Therefore, the distance between the positions at
time i is given in the equation 5.
d
i
= |d
i
| =
q
(p
i
− x
i
)
2
+ (q
i
− y
i
)
2
(5)
Another metric is to calculate the optimal spatial
translation d (shift) between T
E
and T
T
. This me-
tric show a closer relationship between two trajecto-
ries (Needham and Boyle, 2003). They define this
metric by equation 6.
µ

D

T
E
+
ˆ
d, T
T

(6)
where
ˆ
d is the average displacement of two trajecto-
ries, calculated by equation 7:
ˆ
d = µ(d
i
) =
1
n
n
X
i=1
d
i
(7)
Finally, to describe the data obtained previously
and evaluate the tracker we are using statistics applied
to displacement, distance and shift between two tra-
jectories. These statistics provide quantitative infor-
mation about distribution of data, such as, mean, me-
dian, standard deviation and, minimum and maximum
values.
The mean is calculated as follows:
µ(D(T
T
, T
E
)) =
1
n
n
X
i=1
d
i
(8)
VISAPP 2006 - MOTION, TRACKING AND STEREO VISION
232

where n is the number of frames. Besides, the median
is obtained by equation 9 and the standard deviation
by equation 10.
median(D(T
T
, T
E
)) =
(
d
n+1
2
if n odd
d
n
2
+d
n
2
+1
2
if n even
(9)
σ(D(T
T
, T
E
)) =
r
P
n
i=1
(d
i
− µ(d
i
))
2
n
(10)
The equations 11 and 12 correspond to the mini-
mum and maximum distance values respectively.
min(D(T
T
, T
E
)) = the small est d
i
(11)
max(D(T
T
, T
E
)) = the largest d
i
(12)
4.3 Surveillance Metrics
We also use the metrics described in (J. Black and P.,
2003), to measure the tracking performance. We ob-
tain the tracker detection rate (TRDR) for each image
sequence. TRDR is obtained by equation 13.
T RDR =
T otal T rue P ositives
T otal N umber of T ruth P oints
(13)
The false alarm rate (FAR) and the TRDR charac-
terize the tracking performance. FAR is given in the
equation 14.
T RDR =
T otal F alse P ositives
T otal T ruth P ositives +
T otal F alse P ositives
(14)
The object tracking error (OTE) is another metric
that indicates the mean distance between real and es-
timated trajectories. OTE is obtained by equation 15.
OT E =
1
N
rg
X
∃i g(t
i
)∧r(t
i
)
q
(p
i
− x
i
)
2
+ (q
i
− y
i
)
2
(15)
5 EXPERIMENTS
We compared the performance of three adaptive
tracking algorithms. These algorithms are robust to
occlusions by using estimation filters. Experiments
were performed on six real image sequences and two
synthetic sequences. We have considered infrared and
visible images for testing. The size of the each frame
is 640x480 for real sequences and 752x512 for syn-
thetic sequences.
Figure 4(a) corresponds to infrared sequence where
a ship is the target (it will be referenced as sequence
1). Target has been tracked on 422 frames and
presents 40 occlusions during its trajectory. In the
Table 1: Coherence path.
Sequence Coherence Path
1 0.157287
2 0.11906
3 0.499223
4 0.500005
5 0.332765
6 0.206209
7 0.045832
8 0.03558
Figure 4(b) (sequence 2) a car is the interest object
for tracking under occlusions in infrared sequence,
the car is occluded in 75 frames of 575 that composes
this sequence. Figure 4(c) (sequence 3) shows a vi-
sible frame from a sequence where the interest object
is a boat that has an horizontal motion, beginning in
the left top corner of the image. This sequence has
474 frames, in this sequence the boat is occluded in
75 frames. Figure 4(d) presents a synthetic sequence,
the pattern size is 35 pixels, the sequence includes 500
frames where 45 frames were used to simulate occlu-
sions.
5.1 Performance and Results
The coherence path obtained of each sequence is pre-
sented in the Table 1. The half of the all sequences
has a high value in the coherence path.
The metrics between two trajectories were applied.
The Table 2 presents results from sequence 1 with
UKF algorithm. Tracker 2 provides the best results
in the evaluation of the tracked trajectory. The truth
trajectory and tracked trajectory are depicted in the
Figure 5. The Figure 6 shows the trajectory of the
sequence 1 with the simulated occlusions. The trajec-
tory obtained with the tracker (Correlation + Centroid
+ Edges) does not maintain a correct tracking of the
truth trajectory. However when the estimation filter is
used, the problem occlusion is solved.
Table 3 and 4 resumes the results obtained on all
sequences using trackers with UKF and IMM filter
respectively. In both evaluations, correlation with gra-
dual update has the best performance.
In the same way, we have applied the surveillance
metrics for sequences. The results for the first se-
quence are showed in the Table 5. Trackers with
UKF filter have high detection rate in comparison
with IMM-based algorithms.
Finally in Table 6, we show the mean of the va-
lues obtained from all sequences. We can observe that
algorithms with UKF filter presents better values of
TRDR even when detect more false alarms.
PERFORMANCE OF ADAPTIVE TRACKING ALGORITHMS
233

(a) Infrared image (b) Infrared image (c) Visible image (d) Synthetic visible image
Figure 4: Examples used for tracking.
Figure 5: Trajectory of the sequence 1.
Table 2: Results of trajectory evaluation 1 using UKF filter.
Tracker 1 2 3
Metric D(TE,TT) D(TE+d,TT) D(TE,TT) D(TE+d,TT) D(TE,TT) D(TE+d,TT)
Mean 5.387765 2.128756 3.073779 1.859341 5.336042 2.060513
Median 5.974945 1.178952 3.206711 0.931958 5.974945 1.148738
Std. Dev 2.104545 2.370386 1.744103 1.96903 1.979448 2.103242
Minimum 0.03705 0.022712 0.03705 0.094509 0.03705 0.051058
Maximum 14.296198 18.806309 13.806778 16.029772 13.788579 12.84164
Table 3: Mean of the results of trajectory evaluation using UKF filter.
Tracker 1 2 3
Metric D(TE,TT) D(TE+d,TT) D(TE,TT) D(TE+d,TT) D(TE,TT) D(TE+d,TT)
Mean 4.9655835 3.8103235 4.7905565 3.8972085 4.4031985 2.99079
Median 3.683516 2.6102135 3.630965 2.588816 3.683516 2.05372
Std. Dev 5.368305 5.4243535 5.2744075 5.5185885 3.1629285 3.07067
Min 0.157494 0.0891545 0.2175505 0.140762 0.157494 0.08424
Max 31.596568 32.6933673 31.0513235 32.2474888 17.2029045 16.5863
VISAPP 2006 - MOTION, TRACKING AND STEREO VISION
234

Figure 6: Trajectory of the sequence 1 with occlusions.
Table 4: Mean of the results of trajectory evaluation using IMM filter.
Tracker 1 2 3
Metrics D(TE,TT) D(TE+d,TT) D(TE,TT) D(TE+d,TT) D(TE,TT) D(TE+d,TT)
Mean 4.71262 3.886235 4.659543 3.985114 4.1161075 3.16768925
Median 3.2919788 2.59886025 3.54485625 2.69846925 3.42166425 2.27020875
Std. Dev 5.661622 5.61342775 5.52120525 5.6409165 3.59892 3.4811025
Minimum 3.092654 0.183317 0.2427665 0.22414025 0.05414675 0.09454175
Maximum 29.32515 30.695778 29.211101 29.919655 16.681788 18.091328
Table 5: Performance results on sequence 1.
Filter UKF IMM
Tracker 1 2 3 1 2 3
TP 416 419 420 414 416 415
FP 7 4 3 9 7 8
TRDR 0.983452 0.990544 0.992908 0.978723 0.983452 0.981087
FAR 0.016548 0.009456 0.007092 0.021277 0.016548 0.018913
OTE 5.745404 3.246693 5.635883 5.658996 3.258433 5.579214
Table 6: Tracking performance.
Filter UKF IMM
Tracker 1 2 3 1 2 3
TP 685.75 684.25 691.75 687.5 687.5 688
FP 60.75 62.25 54.75 59 59 58.5
TRDR 0.92566 0.92119 0.93981 0.92009 0.92009 0.92111
FAR 0.74364 0.07882 0.06019 0.07992 0.07992 0.07881
OTE 5.41712 5.28336 4.73812 5.07317 5.07317 4.51656
PERFORMANCE OF ADAPTIVE TRACKING ALGORITHMS
235

6 CONCLUSION
In this paper we present three adaptive tracking algo-
rithms. The trackers are: (1) MSFNCA + Centroid,
(2) MSFNCA + Centroid + Edges, and (3) Correla-
tion with gradual update. Trackers adapt to different
conditions by means of performance metrics, which
indicates the best correlation, reducing the possibility
that drift problem occurs.
Moreover, these algorithms have the capability to
follow a target trajectory even under occlusions using
UKF and IMM filters and using a constant accelera-
tion and constant velocity motion models. We ob-
tain the coherence path for each sequence assessing
its complexity before apply the tracking algorithms.
The tracking performance was measured on real and
synthetic sequences. We used metrics that compare
the truth trajectory and the trajectory tracked. Further-
more, we evaluate the algorithms by calculating both
false alarms and correct detections. Correlation with
gradual update improves the tracking and increase the
adaptability to changing environments. The UKF fil-
ter had a slightly best behavior than IMM estimator
even when an occlusion problem occurs.
REFERENCES
Bar-Shalom, Y. and Blair, W. D. (2000). Multitarget Multi-
sensor Tracking, Applications and Advances. Artech
House, Norwood, Ma., volume iii edition.
D. Hall, J. Nascimento, P. R. E. A. P. M. S. P. T. L.-R.
E. R. F. J. S. and Crowley, J. (2005). Comparison
of target detection algorithms using adaptive back-
ground models. In Proceedings of International Work-
shop on Performance Evaluation of Tracking and Sur-
veillance.
Djemel Ziou, S. T. (1998). Edge detection techniques an
overview. In International Journal of Pattern Recog-
nition and Image Analysis.
J. Black, E. T. and P., R. (2003). A novel method for video
tracking performance evaluation. In Joint IEEE Int’l
Workshop on Visual Surveillance and Performance
Evaluation of Tracking and Surveillance (VS-PETS),
pp. 125-132. IEEE.
Jae-Soo Cho, D.-J. K. and Park, D.-J. (2000). Robust cen-
troid target tracker based on new distance features in
cluttered image sequences. In IEICE Trans. Inf. &
Syst, Vol. E83-D, No. 12.
Julier, S. J. and Uhlmann, J. K. (1997). A new extension of
the kalman filter to nonlinear systems. In Proceedings
of SPIE AeroSense Symposium. SPIE.
Kishore, M. S. and Rao, K. V. (2001). Robust correla-
tion tracker. In Academy Proceedings in Engineering
Sciences, Sadhana, Vol. 26, Part 3, pp. 227-236.
L. M. Brown, A. W. Senior, Y.-l. T. J. C. A. H. C.-f. S.
H. M. and Lu, M. (2005). Performance evaluation of
surveillance systems under varying conditions. In Int’l
Workshop on Performance Evaluation of Tracking and
Surveillance. IEEE.
Li, X. R. (2000). A survey of maneuvering target tracking:
Dynamic models. In Proceedings of SPIE Conference
on Signal and Data Processing of Small Targets. Vol.
4048, p. 212-235. SPIE.
Needham, C. J. and Boyle, R. D. (2003). Performance eva-
luation metrics and statistics for positional tracker eva-
luation. In Proceedings of ICVS 2003, LNCS 2626, pp.
278-289. Springer-Verlag.
Ronda, V. and Shue, L. (2000). Multi-mode signal proce-
ssor for imaging infrared seeker. In Proceedings of the
SPIE, Acquisition, tracking and pointing. Vol. 4025.
pp. 142-150. SPIE.
Sezgin, M. and Sankur, B. (2004). Survey over image
thresholding techniques and quantitative performance
evaluation. In Journal of Electronic Imaging. Vol. 13,
Number 1, pp. 146165. SPIE.
Smith, S. M. and Brady, J. (1997). Susan- a new approach to
low level image processing. In International Journal
of Computer Vision, Vol. 23, No. 1.
Tao Yang, Stan Z. Li, Q. P. and Li, J. (2005). Real-time
multiple objects tracking with occlusion handling in
dynamic scenes. In IEEE Computer Society Confe-
rence on Computer Vision Pattern Recognition (CVPR
2005). Vol. 1, 20-25. pp. 970-975. IEEE.
Tine Lefebvre, H. B. and Shutter, J. D. (2004). Kalman
filters for nonlinear systems: a comparison of perfor-
mance. In International Journal of Control. Vol. 77,
Number 7, pp. 639-653(15).
Xu, M. and Ellis, T. (2002). Partial observation vs. blind
tracking through occlusion. In Proceeding of the 13th
British Machine Vision Conference (BMVC 2002).
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