www.ijecs.in
International Journal Of Engineering And Computer Science ISSN:2319-7242
Volume 4 Issue 2 February 2015, Page No. 10325-10332
Identify Obstacles of Different Types in the Path of UGV Using
Region Based Image Segmentation
Rajinder Kaur, Amanpreet Kaur
Department of Information Technology
Chandigarh University, Mohali, India
rajinder1023@gmail.com
Assist. Professor, CSE Department
Chandigarh University, Mohali, India
amanpreet_boparai@yahoo.co.in
Abstract— Unmanned ground vehicle is a smart autonomous vehicle that is mainly capable to do tasks without the need of human
operator. An automated vehicle work during off road navigation and mainly used in military operation such as detecting bombs, border
patroling.These types of automated vehicles are required even in driving road vehicles where human errors cause major fatal loss of life
and property. For this purpose the functionality of unmanned ground vehicle can be enhanced by using region based image
segmentation which will help to identify the obstacles that come in the path of UGV.In this paper, Region based image segmentation
algorithm is proposed to identify obstacles (car, human, tree etc).This method is compared with edge based image segmentation method
based on three parameters (angle of projection, angle of disjunction and angle of disjunction). Based on comparisons, region based
image segmentation algorithm is capable of producing more accurate results as compared to edge based segmentation algorithm.
Keywords—Image segmentation, Unmanned
vehicle, Region, edge, sensor, remote,
autonomous,segments.
ground
I. INTRODUCTION
Now a day, UGV has been used in different applications like
military and civilian operations, border patroling,
surveillance, law enforcement, hostage situation, and police
for some specific mission to detecting and diffusing bombs.
It has the ability to detect obstacle [1, 2, 15].UGV is a smart
autonomous vehicle that is capable to do tasks in a
structured or unstructured environment without the help of
human operator. It use different types of sensors to sense the
structured or unstructured environment then based on sense
it takes the action and then pass the sensed information to
the different computer operator in the different location
through the communication medium. This type of automated
vehicle can carry anything that human can’t do easily [1, 2,
3, 5].
2) Autonomous Operated: I It is an autonomous vehicle that
mainly operates without any help of a human operator.
These types of vehicle use sensors to sense the environment
and control algorithms are used to take an action to achieve
a goal based on senses. It has the ability to learn
autonomously. For example, a Vislab’s autonomous car. [1,
2]
B. How Does UGV Works
An autonomous vehicle, a sensor is fitted over the vehicle
which senses the structured or unstructured environment and
then on the basis of these senses, it takes a decision to
achieve a goal and then pass the output to the computer
operator which is at different location through
communication media whose output is checked by a human.
Autonomous
vehicle
Sensor fitted
over vehicle
Sense
Environment
A. Classes of UGV
UGV is mainly classified into two classes:
1) Remote operated
2) Autonomous operated
1) Remote Operated: It is a vehicle that operates with the
help of a human operator through a communication media.
The tasks which are executed by it be observed through the
operator using direct visualization or sensor such as a digital
video camera. For examples a toy remote control car,
explosives and bomb disabling vehicles. [1, 2]
Forward
information to
human operator
Take a
decision
Use
control
algorithm
Fig 1: How does unmanned ground vehicle works.
Section II describes the previous work that has done in UGV
and image segmentation. Section III describes proposed
work and section VI contains experimental results. Section V
Contains conclusion.
Rajinder Kaur, IJECS Volume 4 Issue 2 February, 2015 Page No.10325-10332
Page 10325
II. RELATED WORK
There are different types of techniques are used in the field
of unmanned ground vehicle to detect and avoidance of
obstacles, but there are many problems in existing
techniques that are discussed in the following survey:
K.Alonzo et al. [4] presented a paper “Obstacle detection for
unmanned ground vehicles: A progress report” in which
they have developed a real time stereo vision system that is
used to sense environment geometry. This system can work
under any condition like night, day and in low visibility. But
stereo is computationally very expensive for unmanned
ground vehicle and also it can identify obstacles only during
off road navigation and it can work in a limited range.
Gavril et.al, presented paper “Real-time object detection for
smart vehicles” [20] .This paper presents an efficient shapebased object detection method based on Distance
Transforms and describes its use for real-time vision onboard vehicles. This method can used to detect objects of
arbitrary shapes The method uses a template hierarchy to
capture the variety of object shapes; efficient hierarchies can
be generated offline for given shape distributions using
stochastic optimization techniques. This method is not
possible to provide an analytical expression for the speedup, because it depends on the actual image data and template
distribution.
B.Francois et.al[5] in this paper “Video-based event
recognition: activity representation and probabilistic
recognition method” authors presented a recognition method
which is mainly used to examine events that can be detected
and segmented from a video by using a probabilistic
analysis that describes the features of moving objects i.e.
shape, motion and trajectory. But the main problem of these
methods is tracking a crowd of people is a very difficult due
to naturally occlusion of the body parts.
Antonio et.al [9] presented a paper “Visual surveillance by
dynamic visual attention method” in which authors proposed
a dynamic visual attention method which is used to divide
the view into moving objects i.e. vehicles, pedestrians and
background. The main problem with this algorithm is that it
can extract only those frames from a video whose situation
is already predefined.
A.Broggi et.al [6] presented a paper “VisLab and the
Evolution of Vision-Based UGVs” in which they have
proposed a terromax vehicle which could move
autonomously only up to 68kmph and it can’t work during
the night and its performance is not impressive because of
vehicle size and height.
J.Pangyu [10] presented a paper “Local Difference
Probability (LDP)-Based Environment Adaptive Algorithm
for Unmanned Ground Vehicle” in which authors proposed
a LDP Algorithm which is used for detection and
recognition of road area. This method solves the problems
that arise during generic classification and/or predefined
model-based road-detection methods. This method can be
worked in known or unknown environment, even different
condition of road, image quality of images is different and
different angles of camera. But the main problem of this
algorithm is that it is only used for road area detection and
recognition. This can’t detect different type of obstacles.
B.H.Udaya et.al [11] presented a paper “An Autonomous
Unmanned Ground Vehicle for Non-Destructive Testing of
Fiber-Reinforced Polymer Bridge Decks” in which authors
proposed a Non- Destructive Testing FRP technique which
uses two IRT and GPR algorithm to enable the UGV to
gather important information which is used to detect
together air and water-filled defects. It is mainly used to
minimize the human errors .But it is not an entirely efficient
examination system.
B.Mckinley et.al[7] in which authors presented a paper “A
Real-Time, Interactive Simulation Environment for
Unmanned Ground Vehicles: The Autonomous Navigation
Virtual Environment Laboratory (ANVEL)” in they have
proposed a tool i.e. Autonomous Navigation Virtual
Environment Laboratory (ANVEL) which uses video game
and physics-based techniques which are perceptive,
interactive, and actually meaningful for UGV. But it is
mainly operate during off road navigation and UGV can
detect and avoid obstacles in static environment.
A.J.Sterling et.al[12] presented a paper “ A ConstraintBased Approach to Shared-Adaptive Control of Ground
Vehicles” in which authors proposed a Constraint-Based
technique for semi -autonomous vehicles .this technique is
used to recognize, designed, and imposed to make sure that
controlled system can avoids hazards and loss of strength
without the control of a human operator. But this technique
became make the controller unstable to turn the wheels fast
enough to avoid collisions and also the controller system can
avoid hazards during off road navigation.
T.Saurabh [8] presented a paper “Visualization Technique
for Unmanned Ground Vehicles Using Point Clouds” in
which authors proposed a visualization technique for UGV
that is 3D point cloud. Point cloud is a data structure that
commonly used to represent three-dimensional data. It also
gives detail information and uses 3D scanner which scans
environment in front in one plane and perceive the result in
3D point clouds. The Cluster extraction gives help to extract
the clusters in the point cloud which are mainly help to
identify the objects of interest i.e. Bomb .But it is mainly
used in unmanned ground vehicle for home hand security.
A.U.J.Mewes et.al[16] presented a paper "Improved
watershed transform for medical image segmentation using
prior information “in which they have proposed a new
segmentation algorithm which is able to overcome the
problems of over segmentation, finding of important areas
with small contrast boundaries, detection of slight structure
which is poor. The algorithms are used in two important
applications: knee cartilage and white matter/gray
segmentation in MR images. The method provides fine
accuracy on these two applications. But it is only used in
different medical image segmentation problems.
F.Guoliang et.al [15] presented a paper “Segmenting human
from photo images based on a coarse-to-fine scheme” in
which authors proposed an effective algorithm which is
coarse-to-fine segmentation. It is mainly working on static
images to segments human body without human
intervention segment. Multicue coarse torso detection
Rajinder Kaur, IJECS Volume 4 Issue 2 February, 2015 Page No.10325-10332
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algorithm (MCTD) and multiple oblique histograms (MOH)
are two algorithms which are based on segmentation.
MCTD algorithm is used to identify torso and MOH is a
strong iterative algorithm which is used to get better lowerbody segmentation. This method is very simple and makes
use of a face detector which is able to find only head
situation currently. It is not used for face orientations.
A.Smolic et.al [14] presented a paper “Distinguishing
Texture Edges from Object Boundaries in Video” in which
authors presented a very simple method that is used to
distinguish object boundaries edges from texture edges in
video. It is simple to implement, & performs well on a
variety of datasets. This method is easy to implement and
allows for texture/object boundary separation but it is not be
sufficient to truly determine all object boundaries.
H.Shi-Min et.al [17] presented a paper “Timeline Editing of
Objects in Video” in which authors proposed a novel realtime method for editing object motions in video. The main
function of this algorithm is to adjust the times to match the
object interaction with respect to the background.
Preprocessing is required to separate the moving objects
from the background but it is very time consuming. This
method is not suitable for videos where it is not easy to
extract number of objects from frames means to separate the
moving objects and background .it works only on images.
Sometimes this method is failed to follow and separate
foreground or background. This method doesn’t fulfill the
requirements of the user when the scene is complex.
Correia et.al [18] presented a paper “Objective Evaluation of
Video Segmentation Quality” in which they have proposed a
parameters to evaluate the quality of segmentation .these
parameters are able to calculate approximately quality of
segmentation according to what a human wants to observe.
Video segmentation is a method which controls the quality
of segmentation by identification of a number of objects
creates a partition for a video is necessary. This method used
to calculate approximately segmentation algorithms’
performance by applying on different applications. It can be
work either for a single object or for on the whole partitions.
This method generally does not address the temporal aspect
of video sequences.
Brejl er.al [21] presented a paper “Object localization and
border detection criteria design in edge-based image
segmentation: automated learning from examples”. This
paper provides a fully automated model-based image
segmentation method. Information which is necessary to
perform image segmentation is automatically derived from a
training set. The two models used which are objects shape
model and border appearance model to segment images. In
the first model, an approximate location of the object of
interest is determined. In the second model, accurate border
segmentation is performed. It finds objects of arbitrary
shape, rotation, or scaling and can handle object variability.
But this model is not well suited for manual outlining of the
3-D surfaces due to some of the difficulties include the
complexity and time requirements.
Chien et.al [22] presented a paper “Efficient moving object
segmentation algorithm using background registration
technique”. In this paper, they have proposed an efficient
moving object segmentation algorithm which is suitable for
real-time content-based multimedia communication systems
First, a background registration technique is used to
construct a reliable background image from the frame. The
moving object region is then separated from the background
region by comparing the current frame with the constructed
background image. Finally, a post-processing step is applied
on the obtained object mask to remove noise regions and to
smooth the object boundary. In situations where object
shadows appear in the background region, a pre-processing
gradient filter is applied on the input image to reduce the
shadow effect. The main problem of this algorithm is that its
computation complexity is very high because both the
watershed algorithm and the motion estimation are
computationally intensive operations.
D. Shih-Huai et.al [19] presented a paper “A Neuro-Fuzzy
Approach for Segmentation of Human Objects in Image
Sequences” in which authors proposed a neuro-fuzzy
approach which is used for automatic take out of human
objects in video streams based on spatial and temporal
features but this method can extract only one person in a
video stream. But the main difficulty of this method is that
it’s processing time is high.
III. PROPOSED WORK
To overcome the problems of existing technique, region
based image segmentation algorithm is proposed which used
to identify different types of obstacles with the following
steps:
 Record a video which consists of road, cars,
humans & trees using camera.
 Extract the appropriate frames from the recorded
video which meet the requirement of preprocessing. For example blurred and distorted
images will not make it good.
 Convert extracted frames into gray level.
 Applying region based segmentation algorithm to
detect different objects in the image.
 Bounding the different segmented objects (i.e. cars,
human) in proper rectangular boxes in the frame.
 Implementing an efficient Decision making table
for deciding the moves of the vehicle.
 Compare proposed segmentation technique with
existing technique.
Record a
video
Extract frames
form video
Compare region
based
segmentation with
existing techniques
Bound the
segmented
obstacles into
rectangular box
Convert frames
into gray level
Apply region
based
segmentation
Fig 2: Data flow diagram.
IV. EXPERIMENTAL RESULTS
Image segmentation which is a method of separating an
image into several segments that is set of pixels that collect
the meaningful information from segment part which is
Rajinder Kaur, IJECS Volume 4 Issue 2 February, 2015 Page No.10325-10332
Page 10327
supportive to take a decision. Image segmentation is widely
used in many fields such as object recognition, image
compression, medical, satellite.
A method of image segmentation that is region
based is proposed which helps to identify objects (cars,
human, trees etc.) that comes in the path of unmanned
ground vehicle during on road navigation and also some
parameters which are angle of projection, angle of
disjunction, no of segmented region that are used to
calculates the moves of vehicles. Using this method,
different types of objects like cars, human and tree are
identified. After that, comparison is performed between
region based segmentation and edge based segmentation.
Based on comparisons, the region based segmentation
provides more accurate results than edge based
segmentation on the basis of parameters. Edge segmentation
does not work properly on parameters because it does not
calculate distance properly between vehicle and identified
obstacles and also the velocity of identified object. The
experimental is performed on ten different images. The
results are calculated based on three parameters: angle of
projection, angle of disjunction and number of segmented
region. Comparative study includes region base
segmentation and edge base segmentation technique.
Fig1 shows the original image which is extracted from
video. In fig2 original image is coverted into gray level.
Gray level consists of only white and black pixel. So it is
easy to segment a gray color image. Fig3 shows that apply
region based segmentation to identify objects that appear in
the image means come in front of vehicle. In fig 4, place the
segmented objects into rectangular box. Angle of projection
which is the distance between vehicle and identified object
that is human is 53 units. Angle of disjunction is the velocity
of identified object is 51 units and number of segmented
region is 45.
Fig5 shows the original image which is extracted from
video. In fig6, original image is converted into gray level. In
Fig7, region based segmentation to identify objects that is
car that appears in the image means come in front of
vehicle. In fig 8, place the segmented object into rectangular
box. Angle of projection between vehicle and identified
object is 10 units. Angle of disjunction is the velocity of
identified object car is 16 units and number of segmented
region is 170.
Fig 1: original image
Fig 2: Gray scale image
Fig9 shows the original image which is extracted from
video. In fig10, original image is converted into gray level.
In fig11, region based segmentation to identify objects i.e.
tree that appear in the image means come in front of vehicle.
In fig 12, place the segmented the objects that is tree into
rectangular box. Angle of projection between vehicle and
identified object is 0 units because tree is on the right side of
vehicle. Angle of disjunction is the velocity of identified
object i.e. tree is 0 units and number of segmented region is
170.
Fig13 shows the original image which is extracted from
video. In fig14, original image is converted into gray level.
In fig 15, region based segmentation to identify objects that
is car that appears in the image means come in front of
vehicle. In fig 16, place the segmented object into
rectangular box. Angle of projection between vehicle and
identified object is 1 unit. Angle of disjunction is the
velocity of identified object car is 16 units and number of
segmented region is 170.
Parameters Analysis:
 Angle of Projection: It is used to find out the
distance between identified obstacles and vehicle.
Its value should be large means distance between
identified object (i.e. car, tree, human etc.) and
vehicle be more.
 Angle of Disjunction: This parameter is used to
find out the speed of vehicle in each frame by
calculating the position of the vehicle. Its value
should be less.
 Number of segmented region: This parameter
segments the images into number of regions based
on threshold value T. It is used to segments the
obstacles like cars, humans etc. Its value should be
less because it is easy to cover each pixel in an
image as compared to large number of segmented
regions.
Fig 3:Segmented image
Rajinder Kaur, IJECS Volume 4 Issue 2 February, 2015 Page No.10325-10332
Fig 4: Obstacle
rectangular box.
into
Page 10328
Fig 5: Original image
Fig 6: Gray scale image
Fig 7:Segmented image
Fig 8: Obstacle
rectangular box.
into
Fig 9:Original image
Fig 10: Gray scale image
Fig 11:Segmented image
Fig 12: Obstacle
rectangular box.
into
Fig 13: Original image
Fig 14: Gray scale image
Fig 15:Segmented image
Fig 16: Obstacle
rectangular box.
into
Rajinder Kaur, IJECS Volume 4 Issue 2 February, 2015 Page No.10325-10332
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Graph 1:Comparsion between region and edge based image segmentation method based on number of segmented region.
This graph shows the comparison between region and edge based segmentation based on number of segmented region. Number of
segmented region is calculated using region based segmentation shows better result than edge based because regions segmented
by region based segmentation is less so it is easy to cover every pixel.
Graph 2:Comparsion between region and edge based image segmentation method based on angle of projection
This graph shows the comparison between region and edge based segmentation based on angle of projection. Angle of projection
is calculated using region based segmentation shows better result than edge because edge can’t calculate distance properly
between obstacles. Edge does not provide good results because in some cases, even distance is exist between identified obstacle
Rajinder Kaur, IJECS Volume 4 Issue 2 February, 2015 Page No.10325-10332
Page 10330
and vehicle but it gives information regarding distance is zero. This algorithm does not calculate the velocity of identified
obstacles.
Table1: comparison between region and edge based segmentation based on parameters
Identify Obstacles using Edge Detection
Identify Obstacles using Region Based Segmentation
S.No
No
.of.
Frames
No. of Segment
Region
Angle of
projection
Angle of disjunction
No of segment
region
Angle of
projection
Frame 1
Frame 2
45
49
53
104
51
51
616
297
156
104
Frame 3
Frame 4
171
170
56
10
46
16
1084
1101
51
0
Frame 5
170
1
1
1056
0
Frame 6
Frame 7
Frame 8
Frame 9
179
139
229
132
145
69
0
138
113
0
0
138
1235
985
1939
943
75
9
0
22
Frame 10
129
101
72
1010
105


Region based segmentation method is compared
with edge based image segmentation method based
on three parameters which are number of
segmented region, angle of disjunction and angle
of projection .
Region based image segmentation method provide
much better results as compare to edge. Because
edge based image segmentation method does not
provide good results because in some cases, even
distance is exist between identified obstacle and
vehicle but it gives information regarding distance
is zero.
V. CONCLUSIONS AND FUTURE SCOPE
UGV is a smart autonomous vehicle that is capable to do
tasks in a structured or unstructured environment without
the help of human operator Different types of algorithms are
used in the field of unmanned ground vehicle for the
detection and identification of the obstacles. But there are
many problems in existing algorithms that have discussed in
literature survey. In this paper, we presented a region based
image segmentation algorithm to improve the functionality
of unmanned ground vehicle during on road navigation.
This algorithm identifies the obstacles (cars, human, trees
etc) in the images which are extracted from a video. After
Angle of disjunction
This technique is not
appropriate to find
the angle of
disjunction of
identified obstacles.
the identification of the obstacles, it uses three parameters
which are angle of projection, angle of disjunction and
number of segmented region to calculate the moves of
vehicle means what decision have to take based on
measurement. This algorithm is tested on several frames
which are extracted from video and compared this algorithm
with edge based image segmentation algorithm.
Based on comparisons, region based image segmentation
algorithm is capable of producing more accurate results as
compared to edge based segmentation algorithm, because
this algorithm can properly identify the obstacles in the
frames. It can also properly calculate the distance and
velocity of identified obstacles.
In future, region based image segmentation algorithm can
be used to identify ditch or more obstacles that will come in
the path of UGV.
ACKNOWLEDGMENT
I would like express my sincere gratitude to my supervisor
Ms.Amanpreet Kaur who assisting me to write this paper. I
thank her for providing me confidence and most importantly
the track for the paper whenever I needed it.
Rajinder Kaur, IJECS Volume 4 Issue 2 February, 2015 Page No.10325-10332
Page 10331
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