Journal of Transportation Technologies, 2011, 1, 123-131
doi:10.4236/jtts.2011.14016 Published Online October 2011 (http://www.SciRP.org/journal/jtts)
A Personnel Detection Algorithm for an Intermodal
Maritime Application of ITS Technology for
Security at Port Facilities
Mouhammad K. Al Akkoumi, Robert C. Huck, James J. Sluss
The University of Oklahoma, Tulsa
Received July 26, 201 1; revised August 23, 2011; accepted September 4, 2011
Abstract
With an overwhelming number of containers entering the United States on a daily basis, ports of entry are
causing major concerns for homeland security. The disruption to commerce to inspect all containers would
be prohibitive. Currently, fences and port security patrols protect these container storage yards. To improve
security system performance, the authors propose a low cost fully distributed Intelligent Transportation Sys-
tem based implementation. Based on prior work accomplished in the design and fielding of a similar system
in the United States, current technologies can be assembled, mixed and matched, and scaled to provide a
comprehensive security system. We also propose the incorporation of a human detection algorithm to en-
hance standard security measures. The human detector is based on the histogram of oriented gradients detec-
tion approach and the Haar-like feature detection approach. According to the conducted experimental re-
sults, merging the two detectors, results in a human detector with a high detection rate and lower false posi-
tive rate. This system allows authorized operators on any console to control any device within the facility
and monitor restricted areas at any given time.
Keywords: Human Detection, Port Security, Smart Surveillance System
1. Introduction
In 2005, over 20 million sea, truck, and rail containers
entered the United States [1]. This increasing number of
containers entering the country poses higher risks for
security breaches and malicious attacks. Physical in-
spect- tion of each and every container on a daily basis
would shut down the entire economy [1]. Furthermore,
many containers coming into the country are stored at the
port for a period of time before being shipped by road,
rail, or barge to their final destination. Storing these con-
tainers in a staging area raises concerns about the secu-
rity of the containers. Thus leading to a need to have a
more efficient system to monitor and protect the port
facility and the cargo. Currently, un-queued video sur-
veillance, vehicle detection, fences and gates, and foot
patrols are the common means for port security. Using
other available technologies, a more efficient security
system can be implemented to allow uninterrupted
freight-flow operations at the port.
Human detection is a fast growing and promising
technique used in various applications to find humans in
given images. Researchers are trying to accomplish this
type of detection using methods that result in high accu-
racy and fast computation. The next sections are written
in the following sequence: sectio n II includes a literature
review of related works. Section III discusses the per-
sonnel detection algorithm and section IV covers the
experimental results of our algorithm. Section VI is a
discussion of futu re wo rk and the conclusion.
2. Background
The benefits of ITS deployments are well known: Im-
proving transportation network efficiency, enhancing
safety and security, reducing cong estion and travel delay,
reducing incident response times, and increasing the ef-
ficiency of both transportation and emergency response
agencies. Today’s typical ITS deployment is a assort-
ment of vehicle detectors, closed circuit television (CCTV)
cameras, fixed and portable message signs, highway ad-
visory radio systems, a web based traveler information

M. K. A. AKKOUMI ET AL.
124
system, weather information, and an integrated commu-
nications network that links the field hardware to system
operators, transportation managers, and emergency man-
agement agencies. In most cases, system control is im-
plemented in a centralized traffic management center that
co-locates the system operators, transportation managers,
response agencies, and their dispatchers [2,3]. Our de-
sign of a distributed, hierarchical, peer-to-peer ITS sys-
tem [4] results in a virtual centralized management cen-
ter where the various system operators, transportation
managers, and incident management agencies can remain
geographically separated throughout the State but still
enjoy most of the benefits provided by a this centralized
management center environment. Another feature of this
system is the use of off-the-shelf equipment and open-
source software to reduce development costs. Addition-
ally, by using standards based network architectures and
protocol converters to communicate with the remotely
deployed sensor devices; the software integration effort
was reduced thereby greatly reducing risks.
In this research, two human detection approaches were
used to create a joint human detector. The two ap-
proaches are the histogram of oriented gradients [5] and
the Viola and Jones approach using a cascade of weak
classifiers [6]. In [6], Viola and Jones proposed the first
approach for detecting objects in images based on Haar-
like features in 2001. This approach has been used pre-
viously to perform face detection, upper and lower body
detection, and full body detection with moderate detec-
tion results [7-9]. While face detection was introduced
first and showed very promising results; Haar-like fea-
ture detection has not shied away from being used in
many other human and object detection algorithms. The
Viola and Jones detector has been used in different ap-
plications to perform fast object recognition. One of the
drawbacks of this detector is its detection inconsistency
with an object's rotation in images.
In [10], Kolsch and Turk proposed a Viola and Jones
detector that performed hand detection with a degree of
rotation. The detector was trained using a dataset that
contained images of hands with different angles of rota-
tion. The results showed an increase of one order of
magnitude in the detection rate of the hand in input im-
age frames. A more advanced version of the Viola and
Jones approach was proposed in [11] by Mita et al. The
authors introduced a new approach for face detection
using joint Haar-like features. The joint features are lo-
cated through the co-occurrence of face features in an
image. The classifiers were then trained using these fea-
tures under adaptive boosting (Adaboost). The results
shown in the paper proved achieving faster detection
time, 2.6 times faster, with similar face detection accu-
racy. The joint Haar-like features also played into re-
duceing the overall detection error by 37% compared to
the traditional Viola and Jones approach.
3. Personnel Detection and Image Processing
Techniques
Establishing exceptionally accurate pedestrian detection
and tracking are two major hurdles facing computer vi-
sion today. Overcoming these challenges can result in
providing more secure surveillance systems to monitor
indoor and outdoor spaces. These smart systems can be
used to enhance security at ports of entry worldwide.
3.1. Haar-Like Feature Pedestrian Detector
The use of Haar-like algorithms simplifies locating all
the desired features. A feature is selected if the differ-
ence between the average dark region pixel value and the
average of the light region is higher than a preset thresh-
old. An example of HAAR features is shown in Figure 1.
As shown in the figure, the features can be used to detect
different pixel orientations throughout a defined region
of interest. A combination of a certain arrangement of
edges can then be identified as the desired object or not.
The features presented in the figure are either 2-rectangle
or 3-rectangle features. Another type of features is the
4-rectangle features that are used in other implementa-
tions of Haar-like features. The feature can be computed
quickly using integral images which are defined as
two-dimensional lookup tables and have the same size as
the input image.
The next step in the algorithm is training the machine
to be able to make decisions whether a pedestrian is pre-
sent in the image region. Adaboost is a machine learn ing
method that uses many weak classifiers to create a strong
classifier. Each weak classifier is assigned a weight to
help strengthen the overall classifier. The weak classify-
ers filter the image region as it passes through th em. If, at
any point, the region is filtered out, then the region is
considered not to have the desired object. The heavily
weighted filters come in first to make the process much
quicker and annihilate negative regions. Figure 2 shows
the overall Viola and Jones detection system.
Figure 1. Haar-like features.

125 M. K. A. AKKOUMI ET AL.
Figure 2. Viola & Jones object detection algorit hm.
The training process is a key stage to formulate strong
classifiers for the Haar-like features pedestrian detector
(HFPD). A combination of training samples is used to
formulate a cascade of classifiers to be used in the detec-
tion process. The complete process goes through 4 main
stages: data preparation, object marking and creating
object samples, training and then finally testing. The
trained detector was used for detecting a pedestrian
lower body region (mainly legs) in a given image. To
train the detector, a set of positive and negative samples
was collected. The positive samples contain on e or more
instances of the human lower body. The negative sam-
ples are the ones that contain no instances of the human
lower body and even no humans. The negative samples
were obtained from an online dataset [12]. The dataset
includes 2977 negative samples of various grey scale
backgrounds with no human or human like objects.
These images are used to train the detector to what is not
the object of interest and ultimately improves th e overall
detection ra te. The wider the range of b ackgroun ds being
used the lower the false positive rates are and the
stronger the classifier would be.
The positive samples were taken in a lab environment
with different backgrounds. Three detectors were trained
using 890, 1890 and 2890 p ositive samples, respectively.
The goal is to try various numbers of positive images and
compare the results. One might think that increasing the
number of positive samples would result in a stronger
cascade of classifiers but that’s not always the case.
There are several factors that determine the strength of
the cascade and these include but are not limited to: the
type of object being detected, the backgrounds of the
positive samples, Object rotation, and Object scaling.
The lower body samples are taken from different view-
points and appear in different poses. The illumination is
kept almost the same with minor differences. The posi-
tive samples were taken using a high definition camcor-
der with a 1280 × 720 p ixel resolution. The resolutio n for
these images is not a factor since all the images are re-
scaled during the training process. These positives will
later be used to specify where the location of the object
of interest is precisely. Various poses of the lower body
were captured to strengthen the cascade to overcome the
rotation drawback of Haar-like features. The images used
in the training process are converted to grey scale, thus
no color constraints are taken into consideration. The
next step prior to starting the training the detector is to
mark the legs in a bounding box in every positive sample
and save its coordinates. Then a vector file for the posi-
tive samples is created. This vector file is an output file
that contains information regarding the generated sam-
ples. The training process time varies according to sev-
eral factors, among these are: the number of training
samples being used, the number of stages the cascade
needs to cover, the memory allocation for the process,
and the processor speed. On average, it took between 2
to 4 hours to train the lower body detectors. Three cas-
cades were trained with 890, 1890 and 2890 positives
respectively and 2977 negatives. 50 images from the
INRIA online dataset were chosen at random for testing
[13].
3.2. HOG Pedestrian Detector
The Histogram of Oriented Gradients (HOG) detection
approach was first introduced in 2005 and focused on
detecting objects based on their edge orientations. The
HOG approach can be compared to the Scale-Invariant
Feature Transform (SIFT) approach proposed by David
Lowe in 1999 [14]. The two approaches share the same
concept of extracting unique features to help in the deci-
sion-making process of whether the target object is pre-
sent in an image. However, the HOG method segments
the image in a different way and makes use of local con-
trast normalization to improve the overall performance of
the system. Now, HOG is being used in multiple object
detection applications resulting in fast and accurate de-

M. K. A. AKKOUMI ET AL.
126

pixel is calculated based on the direction of the gradient
element at its center. According to [18], a fast way to
calculate the histograms of regions of interest is achieved
by using integral histograms.
tection [15-17]. The first step in the HOG algorithm is
gradient computation. The simplest and most efficient
way to accomplish that, as tested by Dalal and Triggs, is
to apply a 1-D, centered point, discrete derivative mask.
Applying other types of masks such as the 3 × 3 Sobel
mask doesn’t lead to better overall system performance.
The derivative mask system is defined as follows:
In order to pass the computed histograms of gradients
into a classifier, cells are organized in a 3 × 3 arrange-
ment called a block. Creating blocks helps make the al-
gorithm less susceptible to changes in illumination and
contrast. The blocks overlap in an image producing more
correlated spatial information to be used in th e descriptor,
which also improves the overall detection performance.
Figure 3 shows an example of blocks containing 9 cells
inside the detection window. The 3 × 3 and 6 × 6 blocks
worked best for Dalal and Triggs in their experimental
results and believe that varying the block size has less
effect on the detection as does overlapping the blocks.
 
 
Y i,j=X i,j+1X i,j1
Y i,j=X i+1,jX(i1,j)





The equation system contains vertical and horizontal
1D derivative masks that can be applied pixel wise to an
input image X. Y is the output image with the calculated
pixel derivatives on row i and column j. The whole im-
age is scanned to calculate each pixel orientation to be
used in computing the later histograms. The derivative
masks used can be expressed as: Also in some cases, increasing the number of cells
present in the block decreases the overall performance of
the detection system. The rectangular HOG, also known
as R-HOG, can be set with different block dimensions
but are best used in square arrangements. The R-HOG is
adopted in the tested HOG human detector presented in
this chapter. A block is represented by a multi-dimen-
sional feature vector that is used in the classification step.
Block normalization is needed to decrease the required
computation, thus L-2 normalization on the block is done
followed by a renormalization step.
,
1
0
1












1 0 1
After calculating the gradients, the algo rithm defines a
detection window of fixed size (64 x 128 pixels) to scan
the image. The detection window is then divided into a
number of 8 × 8 pixel groups called cells, Figure 3. A
cell can be rectangular or radial in shape and can vary in
size although 6 × 6 pixel group is considered an optimal
solution for human detection. For the purpose of this Each block is normalized and used in the collected
feature vector. Using 2 × 2 cells results in having a 36
dimensional normalized feature vector, since 4-9-bin
histograms were used for the HOG detector. The final
step for the HOG algorithm is to use the feature vector as
input to a Support Vector Machine (SVM) classifier to
perform the decision making. SVM has been used by
many researchers in object detection and segmentation to
deliver a classification method for various objects in
study, the selected cells are rectangular. The next step in
this system finds a 9-bin histogram of pixel orientations
for each cell. The number of orientation bins selected
sug- gests looking at 20 degrees for each pixel orienta-
tion. The range from 0 - 180 degrees, for unsigned gra-
dients, is divided by the 9 bin orientation in which linear
gradient voting is represented. A weighted vote for each
Figure 3. HOG detection window with cells and blocks.

127 M. K. A. AKKOUMI ET AL.
input images [19-21]. Linear SVM is one of the most
common methods used for forming different classes of a
dataset. The HOG algorithm feeds the descriptor vector
to a trained linear SVM to determine human presence in
a given test image. The HOG scheme was tested and
performed extremely well on two datasets: the MIT pe-
destrian database and then on a new dataset created by
Dalal and Triggs called the INRIA dataset. A flow dia-
gram of the HOG method is shown in Figure 4.
4. System Analysis and Results
In all the conducted experiments, three rates were ob-
served: false negative rate, false positive rate, and detec-
tion rate. In this paper, these terms are defined as follows:
a false negative rate is calculated by summing the num-
ber of events where the detector missed a human present
in the image and divide it by the total number of events,
a false positive rate is the number of events where the
detector had found something that it thinks is a human
but it is not divided by the total number of events, and
the detection rate is the number of events where the de-
tector had found a human in the image divided by the
total number of events. In add itio n, in th is pap er an even t
is defined as one of three things: not detecting a human
present in the image, falsely detecting a human, and de-
tecting a human. These rates are determined subject-
tively and through a predetermined number of test im-
ages. The background in the videos for the different sce-
narios was static (i.e., fixed camera positions) to help
overcome any background noise that might affect the
detection rate.
The collected experimental results show the perform-
ance of the combined human detector compared with the
two separated detectors. The feedback system maintained
a high detection rate and decreased the false positive rate
which results in a more robust detector. Indoor and out-
door scenarios with different image resoluti ons are test ed.
4.1. Merged HOG and Haar Detectors Results in
an Indoor Scenario
The first scenario tested for the two detectors was in-
doors, as shown in Figure 5. This scenario was used
previously to test the HOG full body and the Haar leg
detector separately. The collected results showed high
detection rates in both cases and very low false positive
and negative rates. The detection rate for the Haar leg
detector was 93.8% for 210 test images and the false
positives rate was 9.5%. The HOG detector was able to
locate the human in every frame with an insignificant
false positive rate.
Figure 4. Static histogram of oriented gradients approach.
Figure 5. HOG and Haar used in an Indoor scenario.

M. K. A. AKKOUMI ET AL.
128
The test results for the indoor scenario were taken to
show both detectors activities and how the algorithm
works in different cases. For example, the first and sec-
ond frames in the above figure show complete detectio n.
The third, sixth and eighth frames show a detected hu-
man by the HOG detector and missed detection by the
Haar detector as explained in Subsection 5.3.2. The
fourth and fifth frames show two cases of HOG detection
and Haar false detection. Note that in the fourth frame
the false detected leg is the upper body and within the
region of the human. In the fifth frame, a second false
positive is shown by the Haar detecto r behind th e human.
This false positive is discarded during th e feedback mes-
saging algorithm while the other one, which is in the
human detection region, is not. The seventh frame shows
one HOG detection box and three Haar detection circles,
two of which are true detection and one false positive
that falls within the HOG box.
4.2. Merged Detectors Tested on Two Humans in
an Outdoor Scenario
The second scenario used to test the two merged detec-
tors was of two humans in an outdoor scenario . Figure 6
shows the detected false positive and negative results
for both detectors. The first frame shows two HOG
boxes for the two humans and that the Haar detector has
missed both. The second and eightth frames are the
only ones where both detectors agree on spotting both
pedestrians. In the third frame, the HOG detector finds
both humans whereas the Haar finds none and adds a
false positive.
The fifth frame shows both humans detected as one
using the HOG full body detector. In this case, only one
alert is sent. In the fourth, sixth, and seventh frames, the
HOG finds the two humans whereas the Haar detector
only finds one. Two alerts are sent out to the authorized
personnel. When tested separately using 300 test images,
the detectors showed different detection, false positive
and negative rates. The HOG outperformed the Haar
detector in the detection and negative rates by almost
20% for each. Both detectors had approximately the
same false positive rate of 6%. Using the feedback mes-
saging system, a more accurate human detector can be
established by merging the two full and part-based de-
tectors. The feedback system helps decrease the false
positive rate for the combined detector. Table 1 shows
the statistics for all three cases.
Figure 6. Results of applying both detectors in an outdoor scenario.
Table 1. Detection statistics for separated and merged detectors.
Detector Types Resolution
(in pixels) Total Number of Test Images Detection Rate False Positive Rate False Negative Rate Average Detection Time (in ms)
HOG Full Body
Detector 640 × 480 300 97.0% 5.3% 3% 790
Haar Leg Detector 640 × 480 300 77.33% 6% 22.67% 50
Merged Detector 640 × 480 300 97.0% 0.67% 3% 880

129 M. K. A. AKKOUMI ET AL.
Each of the 300 test images must ideally produce two
alerts, one for each human in the captured frame. Thus,
the expected total number of true alerts sent is 600. The
false positive rate can be decreased using information
from both detectors where the human is expected to be.
Therefore, a huge reduction in the false positive rate can
be observed. On the other hand, the negative rate stays
the same as the one for the more accurate detector, which
in this case is the HOG full body detector. The final de-
tection rate for the merged detector is 97%. The detec-
tion time for the final detector is approximately the sum
of the detection time of both detectors in addition to a
small margin taken for the feedba ck messaging system.
4.3. Merged Detectors Tested on Multiple
Humans in an Outdoor Scenario
The last scenario investig ated has multiple humans walk-
ing in an outdoor scene. Again, the two detectors are
applied on several test frames to determine subjectively
the false positive, false negative and detection rates.
Figure 7 shows the results of merging the two detectors.
As expected, the HOG detector produced a detection rate
higher than that of the Haar leg detector. The HOG de-
tection rate was 93.5% while the Haar had a detection
rate of 62.8% for 300 test images. The false positive rate
in both cases was less than 3%. Note that the Haar leg
detector was not able to find all four pedestrians in the
test images. This is due to the training dataset that only
included one instance of the target object for each image.
In this scenario, four humans are walking around and
at times partially or fully occluding one another. Table 2
shows the detection, false positive and negative rates in
addition to the average detection time for each detector.
The detection time is higher than the previous scenario
due to an increase in the video resolution from 640 × 480
to 848 × 480 pixels. The system requires just over a sec-
ond to determine whether one or more humans are pre-
sent in frames of size 848x480 pixels. Ideally, the num-
ber of produced alerts should be 1200, but in this case,
the 300 test images contained 1, 2, 3 or 4 humans per
frame. The total number of expected alerts is 838 alerts.
Note that the detection rate for the merged detector is not
much higher than that of the fu ll body detector du e to the
high negative rate that was not decreased. On the other
hand, the false positive rate was taken out by the feed-
back messaging system. The false positives from both
detectors were not in the same location and also did not
correspond with the location of the moving object given
by the tracker.
Table 2. Detection statistics for multiple human separate and merged detectors.
Detector Resolution
(in pixels) Total Number of Test Frames Detection Rate False Positive Rate False Negative Rate Average Detection Time (in ms)
HOG Full Body Detector 848 × 480 300 93.5% 1.1% 6.5% 1010
Haar Leg Detector 848 × 480 300 62.8% 2.7% 37.2% 70
Merged Detector 848 × 480 300 93.5% 0% 6.5% 1150
Figure 7. Results of applying both detectors for multiple human detection.

M. K. A. AKKOUMI ET AL.
130
5. Future Work and Conclusions
reason for the
ents
nded by a grant from
the Federal Highway Administration and the program is
administered by the Oklahoma Department of Transpor-
ategy for maritime security,” 2010.
s.gov/xlibrary/assets/HSPD13_MaritimeSe
df
dson
04.13
Th size of the input image is the main e
slower detection time, and is due to a bigger number of
visited detection windows required for detection. The
authors believe that downsampling the images can help
decrease the detection time to fit in a model for real-time
or near real-time pedestrian detection. Additionally,
work accomplished with General Purpose Graphical
Processing Units (GPGPUs) indicates processing speed
increases with this kind of application. Based on the ex-
perimental results collected thus far, the authors believe
that combining the two detectors in addition to pre-
processing with an object tracker would result in a robust
personnel detection and tracking system. The first stage
in the system is the tracking stage. The object tracker
identifies moving silhouettes in the video capture and
alerts the user of a potential threat. The second stage in-
cludes the HOG full bod y detector that looks at the loca-
tion of the moving object and determines whether it is a
human or not. The third stage introduces the Haar-like
feature pedestrian detector that tries to find upper and
lower human body regions. The fourth stage starts the
feedback messaging between the detectors to decide
whether the detected region actually contains a pedes-
trian or it’s a false positive. After several iterations, the
system converges and the detection results are collected.
The results collected in this paper are based on several
training and testing data sets. This helps establish a more
generalized solution to the presented challeng es. The two
stages complement one another in such a way that the
detection system is much stronger than the current sys-
tems. The Viola and Jones approach is not a computa-
tionally heavy approach and provides object detection at
different scales and backgrounds. Thus, the feedback
stage will help improve the detection rate without slow-
ing down the overall system. In this paper, we proposed
a low cost distributed ITS-based smart sur- veillance
security system for port security. This system is very
scalable and provides improvements to a major inter-
modal maritime application. Using image processing
techniques security can be enhanced to capture unau-
thorized personnel in restricted areas. Port security op-
erators can rely on alerts produced by the pedestrian de-
tection and tracking system as well as the container
tracking devices to assess port security. These systems
complement the overall security system and integrate
well as building blocks. This security approach can be
used in various applications and sites to improve overall
security nationwide.
6. Acknowledgem
This research program is partially fu
tation. The project is federally sponsored under the
SAFETEA-LU transportation authorization act. This
program is a five year program started in October 2005.
This specific research is part of the Phase III tasks and
deliverables.
7. References
[1] “The national str http://www.dh
curityStrategy.p
[2] R. H. Brakman and J. J. Limarzi, “ITS at the Hu
Valley Transportation Management Center,” IEEE Intel-
ligent Systems, Vol. 19, No. 3, 2004, pp. 8-12.
doi:10.1109/MIS.20
onference,
9/NTC.1993.293012
[3] M. J. Kelly, D. J. Folds and N. Sobhi, “ATMS 2000:
Hybrid Automation or a Lights Out Traffic Management
Center?” Proceedings National Telesystems C
1993, pp. 37-42. doi:10.110
nce In-
p. 886-893.
-511-I-518.
72-76.
[4] R. Huck, J. Havlicek, J. Sluss, Jr. and A. Stevenson, “A
Low-Cost Distributed Control Architecture for Intelligent
Transportation Systems Deployment in the State of Okla-
homa,” Proceedings IEEE International Confere
telligent Transportation Systems, Vienna, Austria, 2005,
pp. 919-924.
[5] N. Dalal, and B. Triggs, “Histograms of Oriented Gradi-
ents for Human Detection,” IEEE Computer Society Con-
ference on Computer Vision and Pattern Recognition,
Vol. 1, 2005, p
[6] P. Viola and M. Jones, “Rapid Object Detection Using a
Boosted Cascade of Simple Features,” IEEE Computer
Society Conference on Computer Vision and Pattern Rec-
ognition, Vol. 1, 2001, pp. I
[7] L. Yun and Z. Peng, “An Automatic Hand Gesture Rec-
ognition System Based on Viola-Jones Method and
SVMs,” IEEE International Workshop on Computer Sci-
ence and Engineering, Vol. 2, 2009, pp.
doi:10.1109/WCSE.2009.769
[8] T. Ephraim, T. Himmelman and K. Siddiqi, “Real-Time
Viola-Jones Face Detection in a Web Browser,” IEEE
Canadian Conference on Computer and Robo
2009, pp. 321-328. t Vision,
/CRV.2009.48doi:10.1109
es, 2010,
[9] D. Hefenbrock, J. Oberg, N. T. N. Thanh and R. Kastner,
“Accelerating Viola-Jones Face Detection to FPGA-
Level Using GPUs,” IEEE International Symposium on
Field-Programming Custom Computing Machin
pp. 11-18. doi:10.1109/CRV.2009.48
[10] M. Kolsch and M. Turk, “Analysis of Rotational Robust-
ness of Hand Detection with a Viola-Jones Detector,”
Proceedings International Conference on Pattern Recog-
nition, Santa Barbara, Vol. 3, 2004, pp. 107-110.
[11] T. Mita, T. Kaneko and O. Hori, “Joint Haar-like Fea-
tures for Face Detection,” IEEE International Conference
on Computer Vision, Beijing, Vol. 2, 2005, pp.

M. K. A. AKKOUMI ET AL. 131
inrialpes.fr/data/human/
on
150-1157.
1619-1626.
[12] N. Seo, “Tutorial: OpenCV Haartraining,” 2008.
http://note.sonots.com/SciSoftware/haartraining.html
[13] N. Dalal, “INRIA Person Dataset,”
http://pascal.
[14] D. Lowe, “Object Recognition from Local Scale-In-
variant Features,” IEEE International Conference
Computer Vision, Vol. 2, 1999, pp. 1
doi:10.1109/ICCV.1999.790410
[15] H. X. Jia and Y. J. Zhang, “Fast Human Detection by
Boosting Histograms of Oriented Gradients,” IEEE In-
ternational Conference on Image and Graphics, 2
683-688. 007, pp.
ter Vision and Pattern Recognition, Vol. 2,
[16] Q. Zhu, M. C. Yeh, K. T. Cheng and S. Avidan, “Fast
Human Detection Using a Cascade of Histograms of
Oriented Gradients,” IEEE Computer Society Conference
on Compu
2006, pp. 1491-1498.
[17] C. Zhou, L. Tang, S. Wang and X. Ding, “Human Detec-
tion Based on Fusion of Histograms of Oriented Gradi-
ents and Main Partial Features,” International Congress
on Image and Signal Processing, 2009, pp. 1-5.
doi:10.1109/CISP.2009.5304536
[18] F. Porikli, “Integral Histogram: A Fast Way to Extract
Histograms in Cartesian Spaces,” IEEE Compute
Conference on Computer Vision ar Society nd Pattern Recognition,
on Information Processing,
ages Using Genetic Algorithms and
IEEE
Vol. 1, 2005, pp. 829-836.
[19] H. Li and J. Cao, “Detection and Segmentation of Mov-
ing Objects Based on Support Vector Machine,” IEEE
International Symposium
2010, pp. 193-197.
[20] E. Pasolli, F. Melgani, M. Donelli, R. Attoui and M. De
Vos, “Automatic Detection and Classification of Buried
Objects in GPR Im
Support Vector Machines,” IEEE Geoscience and Remote
Sensing Symposium, Vol. 2, 2008, pp. II-525-II528.
[21] G. Zhu, C. Xu, Q. Huang and W. Gao, “Automatic Multi-
Player Detection and Tracking in Broadcast Sports Video
using Support Vector Machine and Particle Filter,”
International Conference on Multimedia and Expo, 2006,
pp. 1629-1632. doi:10.1109/ICME.2006.262859