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Innovation to Save Lives and Traffic
Controlling Using Image Processing 1Asha.P.,
2Santoshkumar M.,
3Radhika P.,
4vanishree K.
5Vikram S.,
1UG Student,
2UG Student,
3UG Student,
4UG Student,
5Associate Professor
Computer Science Department,
SKSVMACET, Laxmeshwar,India
_______________________________________________________________________________________________________________________________________________
Abstract: - Movement blockage is a noteworthy issue in urban communities of creating Countries like India. Development in
urban populace and the working class fragment expend vehicles to the rising number of vehicles in the urban communities.
Blockage on streets in the long run outcomes in moderate moving activity, which expands the season of travel, in this manner
be outstanding as one of the significant issues in metropolitan urban communities. Crisis vehicles like Fire truck and s need to
achieve their goal at the most punctual. In the event that they invest a considerable measure of energy in car influxes,
esteemed existences of many individuals might be peril. Here the picture successions from a camera are broke down utilizing
different edge location and protest tallying strategies to get the most effective method. At that point, the quantity of vehi cles at
the crossing point is assessed and activity is proficiently overseen. The movement flag sign ceaselessly sparkles to green the
length of the crisis vehicle is holding up at the activity path. After the vehicle crossed the intersection, consequently the
movement signals take after the past example era of activity signs. This can be actualized in LABVIEW.
Keywords; Image processing, MATLAB, __________________________________________________________________________________________________________________________________
I. INTRO DUCTION
Programmed activ ity observing and observation are crit ical for street use and admin istration. Movement parameter
estimation has been a dynamic research range for the advancement of shrewd Transportation systems(ITS).For ITS
applications activity data should be gathered and appropriated. Different sensors have been utilized to gau ge movement
parameters for refreshing activity data. Attractive circle locators have been the most utilized innovations however their
establishment and upkeep are badly arranged and may ended up noticeably inconsistent with future ITS foundation. It is
all around perceived that vision-based camera framework are more flexible for act ivity parameter estimat ion
.notwithstanding quantitative depiction of street blockage, picture estimation can give quantitative portrayal of
movement status including speeds, vehicle checks, and so on.
Additionally, quantitative activity parameter can g ive us finish movement stream data, which satisfies the prerequisite of
movement admin istration hypothesis. Picture fo llowing of moving vehicles can give us quantitative depiction of activity
stream. In the present work the outlined framework p lans to accomplish the accompanying.
o Distinguish the near ness and nonappearance of vehicle in street pictures.
o Signal the activ ity light to go red if the street is void.
o Signal the activ ity light to make strides toward environmental friendliness in the event of nearness of movement
out and about and the term of g reen light is balanced by the movement thickness.
As an issue of urban movement blockage spreads, there is a squeezing requirement for the presentation of
cutting edge innovation and hardware to enhance the best in class of activity control. Movement issues now days are
expanding a result of the developing number of vehicles and the constrained assets gave by current frameworks. The
least difficu lt method for controlling an activity light uses clock for each stage. Another route is to utilize electronic
© 2017 IJEDR | Volume 5, Issue 2 | ISSN: 2321-9939
IJEDR1702024 International Journal of Engineering Development and Research (www.ijedr.org) 144
sensor with a specific end goal to distinguish vehicles, and deliver flag that cycles. We propose a framework for
controlling the activity light by picture preparing.
Figure1. City traffic halts ambulances
Figure 2. Cleared path for ambulance
The framework will recognize vehicle through pictures as opposed to utilizing electronic sensors implanted in the
asphalt. A camera will be introduced inside specific separations from the movement light it will catch the picture
successions. setting picture of a vacant street as reference picture, the caught pictures are consecutively coordinated
utilizing picture coordinating. At whatever point an emergency vehicle goes into the scope of sensors then it catches the
picture and contrast and the reference picture. on the off chance that it matches with reference picture then the flag will
be controlled and cleared, in order to give an unmistakable approach to pass the emergency vehicle. it spares the lives of
individual by giv ing clear approach to movement.
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II.OBJECTIVE
To provide clear way to the ambulance whenever it enters into the range of camera and to control the
signals by measuring the density of traffic thereby avoiding wastage of time and saving the lives of human
beings.
I I I . M ET H O D O L G Y
Learn ing phase
Testing phase
Figure 3: Architectural Diagram
It includes two stages learning stage and testing stage .In learning stage in the wake of performing
division, highlights separated from all the vehicle p ictures alongside anticipated that yield is displayed
would the neural system. In testing, the rescue vehicle display (toys) tests from untrained arrangement of
tests are utilized to test the created ANN show for acknowledgment. From the test pictures the elements
are separated and given to ANN and the comparing yield is checked.
IV.LITERATURE REVIEW
Writing review is an essential for any venture and it helps growing new ideas for actualizing of the
venture. To complete the venture work in a staged way it is important to lead writ ing review. A venture
requires a decent knowledge about the fundamental ideas and comprehension to support these
prerequisites references have been made to numerous course books.
Ayush Kr.Mittal and Deepika Bhandari proposed," A novel way to deal with actualize Green Wave
System and discovery of stolen vehicles in February 2013 ". Amid surge hours, crisis vehicles like
Ambulances, Police autos and Fire Brigade trucks stall out in jams. Because of this, these crisis vehicles
Image samples Segmentation Feature
Extraction
Knowledge base
Test Images Segmentation
Feature Extract ion
Neural
Network
Classifier
Results
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are not ready to achieve their goals in time, coming about into lost human lives. We have bu ilt up a
framework which is utilized to give freedom to any crisis vehicle by turning all the red lights to green on
the way of the crisis vehicle, consequently giving a total green wave to the coveted vehicle. A 'green wave'
is the synchronization of the green period of activity signs. With a 'green wave' setup, a vehicle going
through a green flag will keep on receiving green flags as it goes not far off. Around the globe, green
waves are utilized to extraord inary impact. Frequently criminal or psychological oppressor vehicles must
be recognized. Notwithstanding the green wave way, the framework will track a stolen vehicle when it
goes through a movement light. Rather than any customary vehicle following framework, in which the
Global Positioning System (GPS) module requires battery control, our following framework, introduced
inside the vehicle, does not require any power. The data with respect to the vehicle must be refreshed in
the framework database. In this way, it is an independent 2-level framework which will help in the ID of
crisis vehicles or whatever other wanted vehicle. It is a novel framework which can be utilized to execute
the idea of the green wave. [2]
Suresh Sharma, A.Pithora, G.Guptha, M.Goel, and M.Sinha distributed, "A RFID System in April 2013".
Movement clog is a noteworthy issue in urban communit ies of creating Countries like Ind ia. Development
in urban populace and the working class portion devour vehicles to the rising number of vehicles in the
urban areas. Blockage on streets in the end brings about moderate moving movement, which builds the
season of travel, in this manner be prominent as one of the significant issues in metropolitan urban areas.
Crisis vehicles like emergency vehicle and fire trucks need to achieve their goals a t the soonest. In the
event that they invest a considerable measure of energy in congested driving conditions, esteemed
existences of many individuals might be in peril. Here the picture successions from a camera are dissected
utilizing different edge identification and question tallying strategies to get the most effective strategy. At
that point, the quantity of vehicles at the crossing point is assessed and activity is productively overseen.
The movement flag sign constantly gleams to green the length of the crisis vehicle is holding up at the
activity path. After the vehicle crossed the intersection, naturally the activity signals take after the past
example era of movement signs. This can be executed in LABVIEW. [3]
Geetha.E, V.Viswanadha, Kavitha.G proposed ,"An Intelligent Auto Traffic Signal Control framework in
July 2014". Activity blockage is one of the real issues to be considered. For the most part Vehicular
activity meets at the intersections of the street and are controlled by the movement sig nals. Movement
signals require a decent coordination and control to guarantee the smooth and safe stream of the vehicular
activity. Amid the surge hours, the activity on the streets is at its pinnacle. Additionally, there is a
probability for the crisis vehicles to stuck in the congested driving conditions. Accordingly; there is a
requirement for the dynamic control of the activity amid surge hours. Thus, I propose a shrewd activity
flag controller. The proposed framework tries to limit the potential outcomes of congested driving
conditions, brought about by the activity lights, to some degree by clearing the street with higher th ickness
of vehicles and furthermore gives the freedom to the crisis vehicle assuming any. The framework depends
on the PIC 16F877A miniaturized scale controller, IR sensors and Radio Frequency Identification (RFID)
innovation. The code for this venture is incorporated in innovative C compiler and the reproduced with
Proteus programming. [4]
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Vis may Pandit, Jinesh Doshi, Dhruv Mehta, Ashay Mhatre and Abhilash Janardhan proposed ,"Smart
Traffic Control System utilizing Image Processing in January–February 2014 ". As the issue of urban
movement blockage spreads, there is a squeezing requirement for the presentation of cutting edge
innovation and gear to enhance the best in class of activity control. Movement issues these days are
expanding a direct result of the developing number of vehicles and the constrained assets gave by current
foundations. The least difficult path for controlling a movement light uses clock for each stage. Another
path is to utilize electronic sensors with a specific end goal to recognize vehicles, and create flag that
cycles. We propose a framework for controlling the activity light by picture handling. The framework will
recognize vehicles through pictures as opposed to utilizing electronic sensors implanted in the asphalt. A
camera will be introduced close by the movement light. It will catch picture successions. Setting picture of
a vacant street as reference picture, the caught pictures are consecutively coordinated utilizing picture
coordinating. For this reason edge identificat ion has been done utilizing Prewitt edge discovery
administrator and as indicated by rate of coordinating movement light lengths can be controlled. [1]
N.Ahmed Surobhi and Abbas Jamalipour proposed, "M2M-Based Service Coverage for Mobile Users in
Post-Emergency Environments in September 2014 ". In a framework based remote system, including
portable clients and vehicles, numerous pivotal and imperative admin istrations are provisioned by an
incorporated server. Nonetheless, because of harmed foundation and expanded versatility brought about
by a crisis, keeping up constant administration scope in such a system can challenge. Albeit a few
expectation based replication techniques have been proposed to accomplish benefit scope through
replicat ion of the focal server, they can't precisely foresee future topological changes and therefore keep
up administration scope in a post-crisis arrange. These topological changes are, actually, straightforwardly
identified with client portability. All things considered, existing versatility models can't reasonably speak
to post-crisis client developments. Thusly, at to start with, this paper proposes a practical portability
display that incorporates clients' post-crisis complex behavioral changes. In this way, this paper proposes a
machine-machine (M2M) organizing based administration scope structure for post -crisis conditions. The
proposed structure performs not just precise expectation of the proposed client portability additionally
ideal rep licat ion, using these forecasts, of the focal server to accomplish constant admin istration scope.
Moreover, the structure requires no supervision and less assets to play out these capacities because of
utilizat ion of the M2M organizing. [5]
M.S.kanikar, S.Prabhu, Rahul Chauhan, Akhileshbhat proposed ," Swarm Intelligence for Traffic Routing
in April 2015". Swarm Technology is fundamentally a framework which chips away at ongoing
conditions and the individuals in the gathering interface with each other in a decentralized way to
accomplish a specific target through self - association. Common illustrations are subterranean insect
states, tutoring of fishes, and so forth. Swarm knowledge is a field of counterfeit consciousness.
Computerized reasoning of machine or programming is what thinks about and creates clever machine and
programming to make everyday existence of people significantly less demanding. Swarm conduct is an
aggregate conduct displayed by comparative sorts of species which all together play out a specific
assignment. Till date swarm innovation is been utilized just for robot - to-robot usage. We are utilizing
swarm innovation in which it utilizes Nano bots to do a particular committed undertaking. Fundamentally
this idea deals with chain of importance of three phases in the way Coordinator (mahaguru), Router
(master) and End gadget (bhakt ) which work in an organized way and can take their own choices.
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Presently in our venture we are executing this swarm innovation with the end goal of movement control
by flag to-flag control. Right now the frameworks that are actualized depend on settled clocks or settled
length signals prompting road turned parking lot. The sett led length clocks can be utilized as a part of
litt ler regions of low movement, yet for bigger region or thick activity intersections can prompt blockage.
Our point is to lessen this issue by actualizing the variable clocks. Contingent on the thickness of activity,
the versatile clocks will be set by utilizing different calculat ions. We are building up a versatile and
disseminated calculation which can without much of a stretch adjust the movement flag arranges. This
venture will likewise build up a reasonable activity flag improvement arranges. Additionally wellbeing
will be given top need in our framework. [6]
V.REQUIEMENT SPECIFICATION:
Programming prerequisites specification(SRS) is a portrayal of a product framework to be created, laying
out utilitarian and non-useful necessities, and may incorporate an arrangement of utilization cases that
depict associations the clients will have with the product.
A. Functional Requirements
Practical necessities are those that allude to usefulness of the framewor k. That is the thing that
administrations it will g ive to the clients.
• It gives the reasonable path to the emergency vehicle to spare the esteemed existences of
individuals.
• Measurement of activity thickness control.
• Easy stream of activity.
• Greater effectiveness of the framework.
B. Non –Functional Requirements
Non practical necessities are those that allude to the non –functionality of the framework. That tells about
how the framework is advantage for the client.
The distinctive non –functional prerequisites are recorded beneath:
B.1 Performance Requirements:
The framework is relied upon to have sensible brief time reaction. As the camera is persistently recording
the video in movement flag, once the emergency vehicle is identified in portion of second the way will be
cleared quickly.
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B.2 Reliab ility:
The framework ought to be 99% solid. Since it might require some support or planning for the some
specific day, the framework does not should be dependable without fail. In this way, 80% unwavering
quality is sufficient.
B.3 Efficiency:
By changing the shade of single it will make room to the emergency vehicle and spares lives.
B.4 Availability:
Camera, database, and neural system class classifier are constantly accessible whenever.
B.5 Maintainability:
The framework ought to be improved for supportability, o r simplicity of upkeep quite far.
C. Hardware Requirements:
• Processor : Pentium IV
• Ram : 512MB
• Hard Disk : 200MB
• Resolution : 1024 X 768
D. Software Requirements:
• Operating System : W indows
• Platform :
• Software Module : MATLAB form 2014 as picture preparing programming
• Interfacing : The interfacing between the equipment
• Computer : A broadly useful PC as a focal unit for different assignments
VI. DATAFLOW DIAGRAM
A data flow diagram (DFD) is a significant modelling technique for analyzing and constructing information
processes. Data-flow diagram (DFD) is a graphical representation of the “flow” of data through an informat ion system.
DTDs can also be used for the visualization of data processing(structured design). On a DTD, data item flow from an
external data source or an internal data store to an Internal data store or an external data sink, via an internal process. A
DTD provides no information about the timing or ordering of processes, or about whether processes will operate in
sequence or in parallel. It is therefore quite d ifferent from a flowchart.
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Data-flow d iagrams provide the end user with a physical idea of where the data they input ultimately has an
effect upon the structure of the whole system from order to dispatch to report. How any system is developed can be
determined through a data-flow diagram. W ith a data-flow d iagram, users are able to visualize how the system will
operate, what the system will accomplish, and how the system will be implemented. A designer usually draws a context -
level DTD showing the relationship between the entities inside and outside of a system as one single step. This ba sic
DTD can be then disintegrated to a lower level diagram demonstrating smaller steps exhib iting details of the system that
is being modelled.
Flow Chart Notations
Symbol Name Function
Start/End An oval represents a start or end
point.
Arrow A line is a connector that
Shows relationships between the
representative shapes.
Input/Output A parallelogram represents input or
output.
Process A rectangle represents a process.
Decision A diamond indicates a decision.
Table 1: Flow-chart notation
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Module 1
NO YES
Module 2
Module 3
Figure 4.Flow Chart Diagram
MODULE 1:
The first module the will be record ing vehicles, the frames will be taken from that records and the same is fed to the PC.
MODULE2:
After taking frames as input, the image is segmented into many parts then feature extraction is done in which colour,
texture, shape, size will be analysed. Then the image is compared with stored images then the signals are passed to
microcontroller unit(MCU).
MODULE3:
If vehicle detected is Ambulance then MCU transfers the signals to the traffic controller room then the signal is toggled
accordingly. If the vehicle detected is not a Ambulance then traffic density is estimated and signal is priorit ized
accordingly by the MCU and the signal is toggled.
Start Camera PC
(Image
Processing)
Data of Ambulance
And Density
Ambulance? Density
Ambulance
detected
Priorities the Lanes accordance
to the density
(Count of vehicles)
MCU
Toggle the
traffic
Signals
End
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VII.ALGORITHM
1: Input: Video frames y1.........yT
2: Output: Target states x1........ ..xT
3: for t=1 to T do
4: if t=1 then
5: Transfer prior for an imal representation.
6: In itialize the classifier with parameter set w1
7: else
8: Transfer prior for an imal representation.
9: Estimate xt from t-th frame.
10: Store target observation corresponding to zt.
11: i f the number of target observations is equal to some predefined threshold then
12: Collect a number of negative samples in the current frame.
13: Use the target observations (positive samples) and negative samples to update
wt.
14: Clear the target observation set.
15: else
16: wt = wt -1
17: end i f
18: end i f
19: end for
VIII.RES ULTS
Traffic clog can be unraveled.
Emergency vehicles can achieve the goal most punctual.
Traffic thickness is consistently screen by video handling and changed over into edges.
The casings are broke down by different systems of picture handling they are fragmented for recognize rescue vehicle
and different vehicles.
Traffic flags constantly shines to green the length of crisis vehicle is gone through the activity and it is permitted t
achieve its goal.
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IX. Identifying and Tracking the ambulance to s witch the traffical signals using MATLAB
Figure 5:Before simulation
Figure 6:after simulation
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X.CONCLUS ION
The review demonstrated that picture handling is a superior method to control the state change of the
activity light. It demonstrates that it can diminish the activity clog and keeps away from the time being
squandered by a green light on a void street. It is likewise more steady in recognizing vehicle nearness
since it utilizes real act ivity pictures. It pictures the truth so it works much superior to those frameworks
that depend on identification of the vehicles metal substance.
ACKNOWLEDGMENT
I here by express gratitude towards Dr.S.V.Gorabal our beloved principal for guiding us in right direction and I thank our
guide Vikram shirol for help ing us to achieve our work.
REFFERENCES
[1] Ayush Kr.Mittal and Deepika Bhandari ,” A novel approach to implement Green Wave System
and detection of stolen vehicles in February 2013 ”.
[2] Suresh Sharma, A.Pithora, G.Guptha, M.Goel, and M.Sinha , “A RFID System in April 2013”.
[3] Geetha.E, V.Viswanadha, Kavitha.G ,”An Intelligent Auto Traffic Signal Control system in July
2014”.
[4] Vis may Pandit, Jinesh Doshi, Dhruv Mehta, Ashay Mhatre and Abhilash Janardhan ,“Smart
Traffic Control System using Image Processing in January–February 2014 ”.
[5] N.Ahmed Surobhi and Abbas Jamalipour, “M2M-Based Service Coverage for Mobile Users in
Post-Emergency Environments in September 2014 ”.
[6] M.S.kan ikar, S.Prabhu, Rahul Chauhan, Akhileshbhat ,” Swarm Intelligence for Traffic
Routing in April 2015”.