The document describes an Adaptive and Autonomous Traffic Management System (AATMS) that uses cameras and image processing to: 1) Calculate traffic density in multiple lanes and control traffic lights accordingly 2) Detect vehicle license plates using OCR to generate a vehicle database 3) Detect ambulances using sound processing of siren frequencies and RF communication to give them a green light corridor

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Project Report
ADAPTIVE AND AUTONOMOUS TRAFFIC MANAGEMENT SYSTEM
(AATMS)
Team ID: NIY-63
Team Mentor: Amit Yadav
Institution: Delhi Technological University (DTU)
Team Members: Nitish Goel, Praneet Soni, Parth Sood, Mukul Aggarwal, Prakhar Agarwal
INTRODUCTION:
The Adaptive and Autonomous Traffic Management System (AATMS) is an Intelligent
Transport System(ITS) and is of considerable interest because of its potential in managing real-time
multi- lane traffic using a camera interfaced with a microcontroller. The project introduces novel
framework for density/number of cars calculation, license plate detection and Ambulance detection.
APPLICATION:
The purpose of this paper is to develop a real-time dynamic traffic management system that 1)
maintains the indigenous and optimized flow of traffic by calculating the density/number of vehicles
in several lanes using standard image processing techniques, 2) recognizes the license plates of the
vehicles using OCR Algorithm and automatically generates their database so that defaulters can be
identified and 3) creates a delay-free corridor for Ambulance(Emergency Vehicle) by identifying it
in a particular lane using Sound Processing techniques and giving green signal all the way through.
The project aims at improving efficiency, safety and minimization of human intervention.
SOFTWARE USED: NI LabView 2013, NI Vision Development Module 2013,
NI Vision Acquisition Express 2013
HARDWARE USED: Led's(Red and Green), NI myRIO, Webcams, Powered USB Hub,
RF Module(434MHz), Switches
DETAILS OF THE PROJECT:
The tasks implemented in the project are:-
 Density calculation/number of vehicles detection
 License plate detection and identification
 Ambulance detection
1) Density/Number of vehicles detection
We have used a model of a 4-way intersection and dummy cars for working illustration. The input is
taken from webcams and the output is presented through led's (1 red led, 1 green led per traffic light)
connected to digital I/O pins of myRIO.
We have also successfully implemented the code on recorded real-time traffic feed from 4 different
intersections which shows the robustness and real time implementation of our project.
The above task is divided into two different subtasks
i. Detection of vehicles in each lane
ii. Traffic lights control algorithm

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i) Detection of vehicles in each lane: Vehicle detection in performed using Background Subtraction
followed by image processing techniques such as thresholding, morphology and particle analysis.
The following code is implemented for each one of the four lanes.
 The background image(reference frame) and the current frame are acquired through Vision
Acquisition Express VI and are converted to grayscale using Color Plane Extraction in
Vision Assistant Express VI.
 The reference frame is subtracted from the current frame using IMAQ Absolute Difference
VI.
 The number of cars are detected after series of image processing techniques in Vision Assistant
Express VI VDM Final3.
Fig: Processing Functions of VDM Final3
VDM Fnal3 VI
1) Threshold1 is used to filter out the pixels of the foreground objects on the basis of the threshold
value.
2) Series of morphological operations are performed to remove the unwanted particles.
3) Particle Analysis calculates the number and the area of the detected objects (in pixels).
(a) (b)

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2) Left turn is always free
3) The duration of forward green signal and right
turn green signal is equal.
In the given figure, during one iteration, Lane 4 has the
active green signal while other three lanes have active
red signal.(T.L.=Traffic Light)
The code for the algorithm is given below:-
(c) (d)
Fig: (a) Background Model; (b) Current Frame
(c) Subtracted Image;
(d) After series of morphology operations
(e) Particle Analysis (e)
ii) Traffic lights control algorithm: The code iterates
cyclically through Lane 1 to Lane 4 and controls the
duration of green signal depending upon the number of
vehicles in that lane. We have taken the assumption 1) that
at a time, only one lane has the active green signal
1
2 3
4
T.L
.

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Every iteration of the while loop involves two sequential steps:
Step 1:
Each of the camera returns the number of cars in the respective lane. All these values are
combined into a bundle which is then converted into a 1D array. Each of the these values is
accessed sequentially starting from index 0 to 3.
The value of index to be accessed is decided by the remainder obtained on division of i (iteration
terminal) by 4. In this way, the value iterates from 0 to 3 and then back to 3.
The element accessed is now subjected to 3 range tests, which help in classifying the
corresponding lane into one of the three categories defined, on the basis of number of cars
detected. After the classification, the corresponding wait time is assigned to the green signal of the
that lane.
Step 2:
The green signal of the selected lane is activated and the program waits for the specified time.
After the wait is complete, the while loop iterates again but the lane selected now is the one
in sequence with previous lane.
Also we have introduced and Override Switch. When the switch is active, each lane is assigned an
equal timing for active green signal. This idea can be used when the cameras have to be uninstalled
for maintenance or any other external factors.
RESULTS: (For the 30th Frame)

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2) License plate detection and recognition: The license plate recognition (LPR) system proposed is
based on two main phases: License plate localization, and Segmentation with OCR.
As an initial step, the license plate must be located in order to read the characters. After locating
the plate, masking is performed to extract only the license plate region out of the whole image.
The extracted portion, which is the license plate, is then forwarded to the segmentation and OCR
phase.
The image below gives the LABVIEW Block Diagram for the proposed system.
Fig.1: LABVIEW Block Diagram of the proposed system.
 Both the Vision Acquisition Express VIs are used to acquire the image of the vehicle.
 The first Vision Assistant Express VI extracts the ROI consisting of only the license plate
characters. It returns a mask for this purpose.
 The mask from the above Vision Assistant is converted to ROI with the use of “IMAQ
MaskToROI”.
 The ROI obtained is used to extract the characters of the license plate in the second Vision
Assistant. OCR is then applied on the segmented characters.
License Plate Localization
The license plate localization phase is a very crucial stage. Failing to achieve this task will not
allow the reading of the plate to proceed. The following subsections explain in detail the two main
phases of the system.
Fig.2: Processing Functions used in the ROI Extractor Vision Assistant Express VI.

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ROI EXTRACTOR EXPRESS VI
 Color plane extraction is used to extract the intensity plane.
 Auto Threshold: Clustering is used to look for bright objects.
 Binary Image Inversion is performed.
 Series of Morphological operations are performed to remove the unwanted particles from
the image.
 Finally, Image mask is used to extract the license plate region based on the approx. location
of the number plate in the image.
(a) (b)
(c) (d)
Fig.3: (a) Original Image; (b) After binary image inversion; (c) After morphological operations;
(d) After image mask.
Segmentation with OCR
OCR is the process by which the system reads characters in an image after separating them in
blocks. The separation process is called Segmentation.
Fig.4: Processing Functions used in the OCR Vision Assistant Express VI.
OCR EXPRESS VI
 Image mask from the previous VI is converted into ROI and applied on the original image
of the vehicle.
 Color plane extraction and threshold is applied on the masked ROI. The threshold method
used is Inter-Variance.
 Binary image inversion is performed and border objects are removed.
 Particle filter is used to remove the particles with smaller area as compared to the
characters.
 OCR can now be applied as only the characters are present in the final image.

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(b)
(c)
(d)
(a)
(e)
Fig.5: (a) Original Image; (b) Masked Image; (c) Threshold Image; (d)After Particle filter;
(e) Final OCR.
RESULTS:

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The above code is implemented on still images for working illustration. We have implemented the
algorithm on real time videos which shows the practical implementation of our method.

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3) Ambulance detection: The Ambulance detection is performed in two steps:
 Detection of presence of Ambulance in one of the four lanes
 Identification of that lane
i) Detection of presence of Ambulance in one of the four lanes:
The presence of Ambulance is detected using Sound Processing techniques. It involves the analysis
of the spectrum of the Fast Fourier Transform(fft) of the sound of the siren of the Ambulance.
The following code detected the sound of the siren of the Ambulance:

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 The ambulance siren oscillates between two dominant frequencies, the code exploits this
acoustic characteristic of the siren in order to identify it.
 First of all the input signal is passed through a band pass filter and signal between 800khz and
1500khz is extracted, because dominant frequencies of siren usually lie in this range. We are
not interested in other frequency components.
 Then we find 5 nodes from this signal and calculate the mean to find approximately the
frequency about which the spectrum is spread.
 If mean is <900khz, the given signal is not the siren and no further processing is done.
 If mean is >900khz , then the signal is further processed.
 The fft of the original signal is obtained and the spectrum is divided into 2 parts: 800khz to
1000khz and 1000khz to 1500khz. The peak is found in each of the two segments.
 The frequency at which the peak occurs and the amplitude at that frequency is checked. If they
are in the prescribed range in both the segments ,then the signal is the siren and the output led
is turned on.
RESULTS:
i) Identification of that lane:
We are using RF modules, working at 434Mhz frequency for the detection of the Ambulance
Lane. The driver in the lane will transmit the lane number to the RF receiver, which is connected
to myRIO . We have used 8-bit address lines as the encryption mechanism so that only the
authorized vehicles can transmit the valid signal. When the driver selects a particular lane the
address lines at the transmitter acquires a unique 8-bit value. Data at the receiver is valid , only
when address pins at receiver and transmitter are same. So there are 4 different 8-bit values of the
address line , each corresponding to one lane. The address lines at the transmitter are achieved
through hardware connections.

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At the receiver, myRIO sequentially assigns the 4 8-bit addresses to the receiver address lines. So
the receiver is identified only when the received address lines matches any one of the four
assigned address lines.
8 Bit
9V Battery
(RF Communication)
D0 D1 D2
D0
D1
CONTROL BOARD Data at receiver is
valid if address pins of
transmitter-receiver match
D1
D0
D0
D1
D1
D0
1
D0
A
D
D
R
E
S
S
L
I
N
E
S
Vcc
RF
TRANSMITTER
RF
RECIEVER
Vcc
LANE CONTROL
SWITCH
ENABLE
D0 D1 D2
myRIO
O/p
8 pins to led's of 4 lanes
8-Bit
Address
Lines

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RESULTS:-