innovation to save lives and traffic controlling using ...innovation to save lives and traffic...

© 2017 IJEDR | Volume 5, Issue 2 | ISSN: 2321-9939

IJEDR1702024 International Journal of Engineering Development and Research (www.ijedr.org) 143

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



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.



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



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]



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.



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.



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.



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



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



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.



IX. Identifying and Tracking the ambulance to s witch the traffical signals using MATLAB

Figure 5:Before simulation

Figure 6:after simulation



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”.

innovation to save lives and traffic controlling using ...innovation to save lives and traffic...

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