density based traffic management using dip -...

ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)

Volume 5, Issue 3, March 2016

759

All Rights Reserved © 2016 IJARECE

“Density based traffic Management using DIP”

ROOPADEVI G HOSUR M.TECH, DIGITAL ELECTRONICS,BITM, BALLARI

HEMANTHAKUMAR.K

ASS PROF ELECTRONICS AND COMMUNICATION

BITM BALLARI

CHAPTER 1

Abstract

The project is designed to develop a density based

dynamic traffic signal system. With the world moving

towards smart cities, one of the major problems faced by

all cities is vehicular road traffic congestions. Traffic

congestion has been causing many critical problems in

most populated cities. Due to these congestion problems,

people lose time, miss opportunities, and get frustrated.

The traffic light durations in the conventional methods

have been constant which turns out to be a big

drawback. Conventional traffic light system is based on

fixed time concept allotted to each side of the junction

which cannot be varied as per varying traffic density .

This paper attempts to address the problem of traffic

congestions caused at traffic signals. Traffic monitoring

is based on density of vehicles which improve the traffic

control system by calculating the density of vehicles on

the road.

Key words: Background modeling, foreground detection,

frame differencing, RGB model.

INTRODUCTION

The count of vehicles on the roads increasing each and

every day hence it is required to manage the flow of traffic

.Now a days traffic congestion is a considerable problem in

the fast growing urban areas. Due to population increase in

large urban areas leads to increase in vehicular density,

leading to traffic jams and other related problems. These

problems include increasing commuting time raise in

transportation cost, delayed services and increase in fuel consumption.

Due to lack in the maintenance of traffic signal and its

control which leads to inept traffic flow. For example if we

consider two lanes with one lane having more number of

vehicles and the other lane with lesser number but both got

same time period of green signal where it is loss of time. By

observing suppose if the time period of green signal is more

for the lane which is having more number of vehicles which

leads to efficient use of time.

Some of the old traffic control methods which involve

magnetic loop detectors, infra-red and radar sensors gives us very less information about traffic and thus we need a

separate systems to count traffic and its surveillance.

Certain cost effective solution like the inductive loop

detector when used on poor road surfaces leads to high rate

of failure less pavement life and abstracting traffic while

maintenance. During fog the infrared sensors are affected

more than the video camera and cannot be used for

surveillance.

In the manual traffic control the individual traffic personnel

is made to stand at each individual junction. Even though

this concept is most reliable as the traffic flow is controlled

based on priority but it created health concern. In the present traffic control technique which is timer based automatic

timer system the traffic flow is analyzed for a certain period

of time, the traffic lights are embedded, it is not reliable as

the traffic flow can never be interpreted and it‟s a non

reliable technology. Sometimes when there is zero density in

lane but still it gives green signal because traffic signals

change depending upon the time interval.

This paper attempts to address the problem of traffic

congestions caused at traffic signals. Traffic monitoring is

based on density of vehicles which improve the traffic

control system by calculating the density of vehicles on the

road.

1.1 Problem Definition With the world moving towards smart cities, the major

problems faced by all cities is

vehicular road traffic congestions. Traffic congestion has

been causing many critical problems in most populated

cities. In the present traffic control technique which is timer

based automatic timer system the traffic flow is analyzed for

a certain period of time, the traffic lights are embedded, it is not reliable as the traffic flow can never be interpreted and

it‟s a non reliable technology.

The solution to the above problem is to have Traffic

monitoring based on density of

vehicles which improve the traffic control system by

calculating the density of vehicles

on the road.

1.2 Proposed Methodologies The methodologies include image processing algorithms and

matlab code which includes moving object detection from

the video, background subtraction, tracking, classification,

and counting number of vehicles.

1.3 Objectives

The main objective is to detect a presence of vehicle on the

lane and counting of vehicles which are present on the lanes.

Depending upon the count of vehicles the traffic signals are

changed. Here we initially do the background subtraction by



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using segmentation with help of surveillance video for

detecting and tracking vehicles.

CHAPTER 2

LITERATURE SURVEY

Intensive research has been done on traffic control system

and a large number of articles and research papers has been

published on this topic since from last few decades. But

most of the systems are inductive loop detectors, infra red

and radar sensors which provide very limited traffic

information and they subject to high failure rate when

installed in road surfaces. Video monitoring has been long

in use to monitor security sensitive areas such as banks,

department stores, crowded public places and borders. Nowadays it is also being used in automatic traffic

monitoring system to control the traffic.

Current traffic control system is based on tracking,

classification and activity analysis.

In [2] Bhadra et al. they have used agent-based fuzzy logic

technology for traffic control situations involving multiple

approaches and vehicle movements. In order to provide real

time based dynamic traffic system this agent based fuzzy logic technology is used. Decisions are made considering

clock time by taking into account of density of vehicles on a

specific lane at a specific time. Busy, moderate, and idle are

the three lane status. Real-time data is collected with

specific time intervals. In Fuzzy logic in order to implement

agent technology its mathematical model is utilized.

In [3] author R.tina‟s objective is to enhance efficiency of present automatic traffic signalling system. By integrating

traditional system with automated signaling system. Using

digital camera mounted on a motor we fetch a artificial

vision from all the lanes and hence detects the vehicular

density on the road .By using PC through microprocessor

using we control the direction of the camera for each lane.

Thus the obtained image of the lane is processed to estimate

vehicular density by using image processing .

In [4] author Farheena Shaikh described his idea to

overcome the problem of traffic congestion on intersection.

at the Traffic Signal system is introduced. Here the prior

objective is to calculate the number of total vehicle present

on the road for smooth flow of vehicular traffic without

traffic jam. And the other objective is to, give priority to the

emergency vehicles in spite of other vehicles on the lane. It

is also helpful to overcome the traffic jam problem to

reducing the delay problem and avoiding congestion. It also

helps in providing the emergency services like Fire Brigade

Vehicle, Ambulance or Police on pursuit at right time. Traffic Signal Management when properly designed,

operated and maintained yields significant benefits like less

congestion, saving fuel consumption. Vehicle emissions are

also reduced and it also improves the air quality.

Pezhman Niksaz et. al.[5] propose a system which uses

image processing technique to count the density of vehicles

and the result message is been shown to inform the vehicles

count on the highway. Operations involved are Image

Acquisition of image, transformation of RGB to gray scale,

image enhancement and morphological operations. The captured image from the camera counts has consecutive

frames and they are compared with the first frame. And

finally , if the density of the vehicles is more than a

threshold, a message is shown. By this message we can

predict the amount of reduction in the traffic jam.

CHAPTER – 3

GENERIC VIDEO PROCESSING FRAMEWORK

3.1.1 Video Frame: The input video format is AVI. AVI

abbreviation is audio video interleave. RIFF (Resource

Interchange File Format) container format is used to store

audio and video data of avi file.An uncompressed PCM

format with various parameter are used to store audio data.

Compressed format with various codes and parameters are used to store video data in avi files.

The aviread, aviinfo, mmreader functions are used to read

the input video AVI format. The videoinput is used to read

the video from webcam.

3.1.2 Preprocessing:

Color Models: Particular colors are specified using

standard color models by defining a 3D coordinate system,

and a subspace that contains all constructible colors within a

particular model. Each color model is oriented towards

either specific hardware (RGB, CMY, YIQ), or image

processing applications

The RGB Model: The three independent primary colors red

green blue in the RGB model makes an image. The standard

wavelength for the 3 primaries are as shown in Figure 3.2.

By specifying the amount of each of the primary component

particular specified color is done Figure 3.3 Cartesian

coordinate system is used to specify color model of RGB

geometry .

Figure 3.2: The standard wavelength of

RGB.



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Figure 3.3: The RGB Model.

Color image smoothing: The concept of image smoothing

can be extanded to full-color images with the princiipal

difference that instead of scaler intensity values, component

vectors must be considered.

We get average of the RGB component vectors in this

neighborhood is given in equation

Where c represents an arbitrary vector in RGB color space.

Therefore, smoothiing averagiing can be done on a per-

color-plane basiss

a) Original image

b) Red component

),(

),(

),(

),(

yxB

yxG

yxR

yxc

xySyx

yxK

yx),(

),(1

),( cc



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c) Green component d)

Blue component

Figure 3.4:Result of processing each RGB component.

Figure 3.5:Image somoothing by 5X5 averaging mask

Color image sharpening

By applying Laplacian operator Images can be sharpened.

Where components are equal to the laplacien of the

individual scalar component of the input vector is called Laplacien vector.

𝛁𝟐[𝒄(𝒙, 𝒚]) =

𝛁𝟐𝑹 𝒙, 𝒚

𝛁𝟐𝑮 𝒙, 𝒚

𝛁𝟐𝑩 𝒙,𝒚

Color segmentation

Images are partitioned into regions by segmentation process

.The objective is to specify a color range of segmented

object in an RGB image. Better results are obtained by

Segmentation in RGB space. Given a set of sample color

point‟s representative of the colors of interest we obtain an

estimate of an “average” color that we need to segment.

Such classification requires a measure of similarity. The

simplest measure is the Euclidean distance. Here arbitrary point in RGB space is denoted by z. The distance between z

and a is given by

D (z,a) = ||z – a||

= [(z-a)T(z - a)]

0.5

= [ (zR – aR)2 + (zG – aG)

2 + (zB – aB)

2 ]

0.5

Where the subscripts R, G, and B denote the RGB

components of the vectors a and z.

3.1.3 Moving Object Detection

Each video processing has different needs depending on

applications and thus requires different treatment .Thus

moving objects are the common things found in them.

Moving objects have got detecting regions which are basic

for vision system to provide focus of attention and simplify

analysis of subsequent steps. Changes in illumination,

weather and climate, repetitive motions can lead to clutter

hence it leads to the problem of motion detection.

Background subtraction and optical flow are used as

techniques to detect moving object.

Background Subtraction

Most commonly used technique for motion segmentation in

static scenes is Background subtraction. By subtracting the

current image pixcel by pixel from a given background

image it detects the moving regions like vehicles and

people. The pixels where the difference is above a threshold

are classified as foreground. To reduce the effects of noise

and enhance the detected region we need morphological

post processing operations such as erosion, dilation and

closing are required. For new images over time to adapt to scene change the reference background is updated.

The background image Bt is updated by the use of an

Infinite Impulse Response (IIR) filter as follows:

Bt+1 = αIt + (1 − α) Bt



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Where α ( [0.0, 1.0]) is a learning constant which specify

how much information from the incoming image is put to

the background.

Temporal Differencing

Difference of consecutive frames by pixel by pixel detects

the moving entity from a video this is called as temporal

differencing .This method is more adaptive to scene

changes; however, it usually fails in detecting whole

relevant pixels of moving object.

Optical Flow

Flow vectors of moving entity over time to find the moving

regions in an image is called optical flow. Motion in video

sequences even from a moving camera are detected by optical flow, but the most of the optical flow methods are

computationally complex and cannot be used in real-time

without specialized hardware.

3.1.4 Object Classification

Pedestrians, vehicles, clutter, etc are the few detected

objects from the given video Moving areas found in video

may correspond to different objects in real-world such as.

Objects are to be tracked for there reliability and analyzed .

The two main methods used for moving object

classification and are 1) Shape-based and

2) Motion-based methods.

Shape-based Classification

The Bounding rectangle, area, silhouette and gradient of

detected object regions are the common features of shape

based classification. Shape based classification is used to

classify vehicles into low and heavy vehicle. Cars and bikes

are low vehicles and trucks and bus are heavy vehicle.

Dispersedness of an object consist of area and its perimeter

as in equation

Dispersedness =Perimeter2/Area

Neural network classifier is used to view dependent visual

features like human and human groups vehicle and clutters.

Motion-based Classification

Temporal motion is used to find classes of an object. These

classes helps to detect objects into rigiad and none rigid.

Self-similarity measure are shown by an object which shows

periodic motion.

3.1.5 Object Tracking

Though Object Tracking is a difficult problem but it still

arouses interest among computer vision researchers

.Correspondence of objects and object parts between consecutive frames of video are established by object

tracking. Object tracking provides cohesive temporal data

about moving objects used to enhance lower level

processing and to enable higher level data extraction.

Applying tracking in congested situations leads to inaccurate segmentation of objects. Erroneous segmentation leads to

long shadow, partial and full occlusion of objects with each

other and with stationary thing in the scene. Two type of

approaches in tracking objects as a whole are

1) One is based on correspondence matching and

2) One carries out explicit tracking by making use of

position prediction or motion estimation.

Vehicle tracking is done by comparing and matching the

features such as size, shape and by calculating the centroid

of the object.

CHAPTER - 4

MOTION SEGMENTATION USING BACKGROUND

SUBTRACTION

The 4 main steps of background subtraction are

preprocessing, background modeling, foreground detection

and data validation. Here in preprocessing first it takes the

raw input video and converts it into a format which can be

used for the further steps. In the background modeling it calculates the frame updates it in a model with the support

of background modeling. Video fram that cannot be

adequately explained by the background model are detected

by foreground detection, and outputs them as a binary

candidate foreground mask. And data validation examines

the candidate mask, eliminates those pixels that do not

correspond to actual moving objects, and outputs the final

foreground mask.

4.1 PREPROCESSING

In preprocessing video the smoothened video is used to

remove the noise and then RGB color model is selected.

Simple temporal or spatial smoothing is used in computer

vision system for early stage of processing to reduce camera

noise. Environmental noise is removed by using smoothing

technique. Data processing rate are reduced by frane-size and frame-rate reduction. Registration of an image between

the one and other frames before the background modeling is

needed then the more number of cameras are used at

different positions or areas. One main problem in the

preprocessing is formatting a data which is used by

background subtraction. luminance intensity, is used by

many of the algorithms which is one scalar value per pixel.

In the background subtraction literature color image, in

either RGB or HSV color space, has become more popular.

In addition to color,to incorporate edges and motion

information spatial and temporal derivatives are u



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Figure 4.2 Pixel level noise removals. (a) Estimated

background image (b) Current image (c) Detected

foreground regions before noise removal (d) Foreground

regions after noise removal

In Fig 4.2 To remove noise caused by the camera

morphological operations such as dilation and erosion are

applied. These operations are used to remove noisy

foreground pixels that do not correspond to actual

foreground regions and to remove the noisy background pixels near and inside object regions that are actually

foreground pixels.

4.2 BACKGROUND SUBTRACTION

This algorithm is used for background modeling. It is very

sensitive hence it detects the all moving elements but it is

very hard to identify when there is changes occur with

environment Non-recursive and recursive are the two

methods used in background subtraction .The non recursive

methods are used for Highly-adaptive operations and

exclude those that require significant resource for initialization. It(x,y) and Bt(x, y) are used to denote the

luminance pixel intensity and its background estimate at

spatial location (x, y) and time t.

Figure4.3: Background Subtraction

In Fig 4.3 first the background B(x, y, t) at time t is

estimated by non recursive technique and then it is subtracted from input frame I(x, y, t). After subtracting the

absolute difference is checked and if it is greater than the

threshold (Th), the foreground mask is estimated.

Non-recursive Techniques

A Sliding-window approach used in non-recursive technique

for background estimation. Image based on the temporal

variation of each pixel within the buffer of the previous L

video frames, estimate the background image. Non-

recursive techniques are highly adaptive as they does not

depend on the history beyond those frames stored in the

buffer.

Frame Differencing

By calculating the difference between two consecutive

images the presence of moving objects determined by

subtraction method. Evaluated background is just the

previous frame. It works only in particular conditions of

objects speed and frame rate and is very sensitive to the threshold (Th) and also calculation is simple and easy to

implement. Depending on the object structure, speed, frame

rate and global threshold, this approach may or may not be

useful.



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B(x , y , t) = I (x , y , t − 1) here the estimated background

is the previous frame.

⇓

|I (x , y , t) − I (x , y , t − 1)| > Th

In the equation 3.1 I(x, y, t) represents intensity value at

position (x, y), at time instance t, I(x, y, t-1) represents

intensity value at position (x, y), at time instance t-1 and the

per-pixel threshold, Th, is initially set to a pre-determined

value. Frame differencing is the difference between

consecutive frames as shown in Figure4.4.

Figure 4.4: Frame Differencing

Figure 4.5: Frame Differencing at different thresholds

(a)Th = 25,(b)Th = 50,(c)Th = 100,(d)Th=200.

The Fig 4.5 shows the effect of threshold in frame

differencing. When the threshold is 200, foreground pixels

are not found as compared to when threshold is 25. When

the threshold increases the number of foreground pixels

found reduces, hence it is diffiicult to get the complet

outline of the moviing vehicle. Thus the identification of the moving vehicle is not accurrate in frame differencing

technique.

Median Filtering

Median filtering is one of the most commonly-used

background modeling techniques. The median point at every

pixel of every frame in the buffer is called as background

estimate. Here we make assumption that the pixel stays in

the background for more then half of the frames in the buffer. It has been extend to color by replacing the median

with the medoid. The problem of calculating the median is

O (nlog n) for each pixel where n is the previous video

frames stored in buffer.

If I(x, y, t-i) represents a frame at time interval t-i, then

background image is obtained by taking its median and it is

given by equation

B(x, y, t) = median {I (x, y, t − i)}

⇓ |I (x , y , t) − median{I (x , y , t − i )}| > Th

Where i ∈ {0 . . . n − 1} and n is number previous frames

For example when n= 10, estimated background and

foreground masks are as shown in Figure 4.6.

Figure 4.6: Estimated background and foreground

masks for n=10

In Fig 4.6 the number of frames taken for background

estimation is 10, hence foreground pixel are not clearly

found. The background estimate is the median at each pixel

location of all the frames in the buffer.

Figure 4.7: Estimated background and foreground

masks for n=20

As in Fig 4.7 number of frames for background registration

is 20, hence foreground objects detected are better than for

n=10.

In Fig 4.8 number of frames for background registration is

50, hence foreground objects are accurately detected as

compared to n=10 and 20.



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4.3 FOREGROUND DETECTION

Candidate foreground pixels from the input frame are

identified by comparing the input video frame with the

background model this is called foreground detection. The

most commonly used approach for foreground detection is

to check whether the input pixel is significantly different

from the corresponding background estimate. Threshold is

chosen experimentally. Threshold is a function of the spatial

location (x, y). The primary step is to recognize “strong"

foreground pixels so that it‟s utter difference with the

background overreach a large threshold. Then, foreground regions are developed from strong foreground pixels by

including neighboring pixels with utter difference greater

than a small threshold.

If It(x,y) and Bt(x,y) represents the input frame and

background frame respectively then foreground objects are

obtained by subtracting background frame from input frame

by satisfying the following equation (3.3).

|It(x, y) – Bt(x, y)| > Th

………… (3.3)

Where Th is the threshold

Fig 4.9 shows the example where an input frame is

subtracted from a given background image Figure 4.3(b)

a

b

Figure 4.9: Foreground detection. a) Input frame, for

given background image b) Input frame after

subtracting with background image, showing foreground

objects.

4.4 DATA VALIDATION

The process of improving the candidate foreground mask

based on information obtained from outside the background

model is called Data validation. The main limitations of

background: 1) Correlation between neighboring pixels are

ignored,

2) Due to moving speed of the foreground objects it is

difficult to match the rate of adaption

3) Non-stationary pixels cast by moving objects such

as moving leaves or shadow are easily mistaken as

true foreground objects.

The false-positive or negative areas scattered invariably

across the candidate mask are the typical issue. Thus by

combining morphological filtering and connected component grouping to eliminate these regions. To

eliminate isolated foreground pixels and merges nearby

disconnected foreground regions need to apply

morphological filtering on foreground masks. Moving entity

of interest should be greater than a certain size. By

connecting-component grouping to recognize all connected

foreground areas, and remove those that are small to

correspond to moving entity. Large areas of false foreground

often occur when the background model modify at a slow

rate than the foreground scene. It fail to detect the part of a

foreground entity which has corrupted the background

modal if the background model adjust too fast, simple approach to reduce these issues is to use more number of

background models running at different adaptation rates,

and periodically cross-validate between other models to

improve production.



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Chapter 5

VEHICLE TRACKING, DETECTION, COUNTING

AND CLASSIFICATON

The video in avi format is processed by selecting RGB

model smoothened by 5X5 averaging masks. After

background modeling by median filtering, the vehicles are

detected, tracked and classified. Speed of the vehicle is also

estimated.

Figure 5.1: Proposed Architecture flow diagram.

In the proposed architecture flow diagram as shown in Fig

4.1, the video is acquired through a stationary camera is

used to acquire the video and the median filtering is used by

background modeling The processing includes:

1) Automatically estimating the primary location of moving

entity,

2) Extricating characteristic details from all moving entity

within site,

3) Tracing identified objects by feature and

4) Categorizing the moving entity into two groups: heavy

vehicle and low vehicle.

By integrating spatial position, motion, shape and color in

tracking system. Changes in the background by integration

are made insensitive by the tracker and interruption of

motion and position of objects. Segment the moving object

blobs, detect the motion and background variation and then

compare the similarity of the object blob with different

templates, thereby tracing the objects.

5.1 MOVING OBJECT DETECTION

The primary objective is to separate the object from its

background. Frame differencing and background subtraction

are the two common methods. Frame differencing is

basically a threshold of difference between the current

image and sequence images by assuming that the

background do not change over successive frames. Some issue occurs when tracing many entity or when an entity

stops in which the moving object is not accurately detected.

Hence background subtraction is used at the cost of

improving the background. By considering a moving entity

will stay at the same point and by using pixel median centre

we make the background for more than half of L where L is

previous frames stored in buffer. The background model for

pixel xt (m, n) at frame t using a length L median filter is

given by equation

xt (m, n) = medianL ( xt−0.5L (m, n),..., xt+0.5L (m, n))

This retains the stationary pixels in the background. When

we identify a large new blob it requires more amount of

memory to store the L frames at the time and the median

filter can make the background.

Background subtraction done on RGB color model.

The RGB Color Model

The RGB (Red, Green, Blue) color model uses a cartesian

coordinate system and forms a unit cube shown in Figure 1.

The Fig 5.2 shows the RGB model in the Cartesian

coordinate system. Gray level is shown with dotted lines

where the red green blue has equal amounts. This diagonal

is referred as gray diagonal. Image capturing, processing and rendering devices use this RGB model and it is in the

hardware form.

Figure 5.2: RGB Model in Cartesian

coordinate system



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Subtract the foreground from the background in each RGB

color channel and then take the maximum accurate values of

the 3 differences as the difference value Diffc in color space.

Diff c = max{ | R f − Rb | , | G f −Gb | , | B f − Bb | }

The equation 5.2 indicate the subtraction of foreground from

background in RGB color channel

The binary foreground pixels F (x, y) are produced by

equation

𝑭 𝒙,𝒚 = 𝟏 𝒊𝒇 𝐃𝐢𝐟𝐟𝐜 > 𝑻𝒉

𝟎 𝒐𝒕𝒉𝒆𝒓𝒘𝒊𝒔𝒆

The resulting foreground contains noise due to the clutter in

the background. Noise is removed by the „close‟ binary

morphological operator. We make assumption that initially

blob contains moving entity and it may be a human being or

bikes, cars or group of people. Where we find the moving

entity or object the rectangle box has been drawn over

it.Figure 4.10 shows sample foreground areas before and

after region connecting, labeling and boxing.

a

b

c

Figure 5.3: Connected component labeling sample. (a)

Calculated background

(b) Present image (c) Filtered foreground pixels and

connected and named regions

with bounding boxes

5.1.2 Noise Removal

The background and the discrepancy image contains the

motion region as well as large number of noises, By using

morphological operations noises are removed which is

caused due to environmental factors, illumination changes,

and during transmission of video from the camera to the

further processing..

Tool for extracting image components is Mathematical

morphology which are useful in the representation and

description of region shape. Morphological techniques for pre- and post-processing are morphological filtering,

thinning, and pruning.

Morphology is a broad set of image processing operations

that process images based on shapes. Structuring

element to an input image, creating an output image of the

same size is done by Morphological operations. Dilation and

erosion are basic morphological operations.

Figure 5.4: Original Picture

In Fig 5.4 shows the original picture with a moving car,

subtract this image with the background image as in

Figure 5.5. The resultant figure after subtraction is

shown in Figure 5.6 which contains the moving blobs

along with noise.



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Figure 5.5: Background Picture

Figure 5.6: Moving blobs

The Figure 4.6 shows moving object with the noise.

There are various factors that cause the noiise in

foreground detection such as:

1. Camera noise 2. Reflectance noise

3. Background colored object noise

Figure 5.7: Denoised Image

The Fig 5.7 is the image after performing the morphological

operations to remove the noise. Noises are removed using

morphological operations.

These operations are applied to remove noisy foreground

pixels that do not correspond to actual foreground regions.

Figure 5.8: Original image Figure 5.9:

Dilation followed by erosion

For example consider the image in Figure 5.8 which is the

original image and Figure 5.9 which shows dilation followed by erosion in which only the non background noise

is removed. If erosion is followed by dilation, then non-

foreground noise (NFN) regions would be eliminated but

non background noise (NBN) would not be eliminated

because the holes inside objects could not be closed.

Figure 5.10 Original image Figure 5.11

Erosion followed by dilation



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For example consider the image in Figure 4.10 which is the

original image and Figure 4.11 shows erosion followed by

dilation in which only the non foreground noise is removed.

5.2 FEATURE EXTRACTION

Two types of features are extracted in each moving blob.

Features of object centroid and color are used for extraction.

Centroid is used for the spatial position of the blob.and it

acts as an important feature for tracing an entity.We

calculate the centroid (𝑋 , 𝑌 ) in binary image I(x,y) by the

equation 4.6.

𝑋 =1

𝐴 𝑥(𝑥 ,𝑦)∊𝑅

𝑌 =1

𝐴 𝑦(𝑥,𝑦)∊𝑅 ……….. (4.6)

Where A is the number of pixels in the blob R.

An object‟s next position is in the neighborhood of its

current position. When the distance from an entity to every

template is minimum then the matching occurs and the

distance is less than the specified low threshold. If distance greater than certain high threshold then it is said to be a new

object. Here high and low threshold is per pixel, pre-

determined value. shape-based information is provided by

shape feature . The shape feature uses length, width and

area of the objects

1)

L = max x (t) − min x (t)

W = max y (t) − min y (t)

Where x (t) is the pixel along x-direction and y (t) is the

pixel along y-direction.

2)

Area A = ∑∑I (x, y)

(x, y)ϵR

Where R is the region of moving blob.

When shape information is not reliable colorist used to

trace. And it is independent of the object size.

5.3 OBJECT TRACKING

The tracing operation compares the features vector Ri,t with

all templates Ti,t −1 (i=1,2…M).The template is increased to

next step if matching is found. The next matching through

an adaptive filter as given in equation 4.7. Where β ( [0.0,

1.0]) is a learning constant which specify how much

information from the incoming image is put to the

background.

Ti,t = βTi,t −1 +(1− β)Ri,t ………………(4.7)

If matching is not obtained for next frames, then a new

template is created TM +1,. A centroid, shape and color.in

this order matching operation is executed.

5.4 CLASSIFICATION

The vehicle classification done based on shape of the object.

Hence shape based classification is used as by calculating

the length and area of the object. Compared to heavy vehicle

such as trucks ,low vehicle such as bike and cars have less

area

vehicles are classified into two categories: cars and non-

cars . Shape-based techniques is used to Separating, say

SUVs from pickup trucks . Lower level work is done by

coarse, classify dimension based at the top level. The final aim of the system is to classify vehicle at more stages of

granularity.

To do the classification depending upon the dimensions of

vehicles, we calculate the actual length and height of the

vehicles.

5.5 VEHICLE COUNTING

The tracked binary image mask1 forms the input image for

counting. To Detect the presence of an object image is

scanned from top to bottom. An input image with masked

binary image is used for count. Count and count register are used to maintain information of registered entity. The

registration of the entity is cross verified in the buffer about

its prior registration. If the entity is not registered it is taken

as new entity and count is increased , if it is in buffer it is

neglected.

This method being used to all image and count of entity is

increased and accuracy is got sometimes object are merged

and treated as single.

Steps to count vehicle

1. Mask in detected to trace an object by traversing.

2. If entity is being newly faced then register in count reg is verified.

3. If the entity is new then its count increased and count reg

is named with new count.

4. Steps are repeated(2-4) until cross verification not

completed

Chapter 6

RESULTS AND DISCUSSION

The database for traffic images is created and the vehicle

count for the images is done by taking median of the pixels

and background



771

a)Image1

b)BackgroundImage

c) Result of image1 after background subtraction

d) Image 2

e) Result of Image 2

f) Results of Image 3

Figure 6.1 Background subtractions for traffic images.

The Fig 6.1 shows the background subtractions of images in

which 6.1 (b) shows the background image, 6.1(a) is the

input image 1, 6.1(c) is the image after background

subtraction with vehicle count = 2, and 6.1(f) shows the

result of image 3 in which the actual number of vehicle

count is 6 but number of vehicles counted after background

subtraction is 4 which is due to occlusion effect.

Image

no

Original

Image

Vehicle

Found

Error

1 2 2 0

2 3 3 0

3 2 2 0

4 1 1 0

5 3 3 0

6 1 1 0

7 1 1 0

8 2 2 0

9 1 1 0

10 1 1 0

11 3 2 1

12 2 2 0

13 4 1 3

14 2 2 0

15 3 2 1

16 6 4 2

17 3 3 0

18 3 3 0

19 3 2 1

20 6 4 2

21 4 4 0

22 4 3 1

23 3 1 2

24 4 3 1

25 3 2 1

Table 1: Vehicles found in Images

Orginal Number of Vehicles :70

Vehicles Found : 55

Accuracy: 78.57%

Vehicle tracking and counting in images is a diffcult task

and produce an error result. It does not give proper count

because background image is fixed and the timing of images

taken varies. The time of background image taken and

foreground image taken will vary, hence it gives improper

vehicle count due to which the traffic signals cannot be

controlled properly. It can be observed from Table 1 that the orginal number of vehicles in images are 70 and the vehicles

found are 55 which gives an accuracy of 78.57%. Hence we

go for background subtraction in video which give a better

video based traffic survivellance.

6.2 FRAME DIFFRENCING BACKGROUND

SUBTRACTION.

The presence of moving objects is found by taking

difference between consecutive frames. The background is

just the previous frame. It works only in particular

conditions of objects speed and frame rate. Here we have to

initially specify the threshold. Depending upon the threshold

the foreground pixels are found. Hence these method is not

used in pratically and we cannot detect fast moving vehicles.



772

Its is difficult to obtain the complete outline of the moving

object.

Figure 6.2 shows the frame diffrencing performed for a

stardard visiontraffic.avi video. In the result shown in Figure

6.1 it can be seen that it is difficult to obtain complete

outline of moving object, due to which it appears as empty

phenomenon at threshold 200. As a result the detection of

moving object is not accurate. Depending on the object

structure, speed, frame rate and global threshold, this

method isnot usefull

a)Threshold=25

b)Threshold= 50

c)Threshold=100

d)Threshold = 200

Fig6.2: Frame Diffrencing at different Thresholds

6.3 BACKGROUND SUBTRACTION BY MEDIAN

FILTERING.

Background modeling by median filtering is initially done

for standard traffic videos in which the vehicle tracking,

vehicle detection, speed calculation, vehicle classification,

and vehicle counting is done.

Video1 of 530 frames, width 640and height 360.



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Figure 6.3: Video1 with counting 1 at frame 160

The Fig 6.3 shows the video1 with counting 1 at frame 160.

The Fig 6.3(a) shows the input frame 160 of video 1, Fig

6.3(b) is the background frame, Fig 6.3(c) indicates the

difference values after background subtraction, Fig 6.3(d) is

the binary mask which contains noise, Fig 6.3(e) is the

denoised mask in which the noise is removed by the

morphological operations, and Fig 6.3(f) shows the output

with the vehicle count 1, and also indicating the speed of the vehicle with which it moves.

Figure 6.4 Video 1with counting 2 at frame 227

Fig 6.4 shows the video1 with counting 2 at frame 227. The

Fig 6.4(a) shows the input frame 227 of video 1, Fig 6.4(b)

is the background frame, Fig 6.4(c) indicates the difference

values after background subtraction, Fig 6.4(d) is the binary

mask which contains noise, Fig 6.4(e) is the denoised mask

in which the noise is removed by the morphological

operations, and Fig 6.4(f) shows the output with the vehicle count 2, and also indicating the speed of the vehicle with

which it moves.

Video 3 viptraffic.avi with 120 frames, width 160 height

120

Figure 6.5: Video2 with counting 1 at frame 60

Fig 6.5 shows the video3 with counting 1 at frame 60. The

Fig 6.5(a) shows the input frame 60 of video 3, Figure

6.5(b) is the background frame, Fig 6.5(c) indicates the

difference values after background subtraction, Fig 6.5(d) is

the binary mask which contains noise, Fig 6.5(e) is the de

noised mask in which the noise is removed by the

morphological operations, and Fig 6.5(f) shows the output

with the vehicle count 1, and also indicating the speed of the

vehicle with which it moves.

Input

Vide

o

Forma

t

Frame

s

Actual

No of

Vehicle

s

Detecte

d No of

Vehicles

Accurac

y

Vide

o 1

RGB 530 4 4 100%

Video 2

RGB 100 4 4 100%

table 2: Accuracy of counting in Videos

Table 2 shows the accuracy of counting the vehicles in the

traffic videos which is recorded through a camera. A fairly

good accuracy is obtained, but some time due to occlusion,

two vehicles may merge together and treated as a single

entity.



774

Chapter 7

CONCLUSION AND SCOPE FOR FUTURE WORK

The background registration technique using a median

filtering and frame differencing is studied. The problem of

selecting threshold for frame differencing is seen hence

median filtering is chosen. Noise removal using

morphological operator have been studied. The project

worked is considered in ideal conditions.

A system has been build to identify, count of vehicles on a road efficiently. Development of a system to trace , count of

vehicles in a lane effectively. We integrate domain

knowledge about entity cleses and time domain static

measure to find entity with various morphological operation

and remove unwanted clutters.

By looking at table 1 we find that actual count of vehicle is

70 but vehicle found are 55 hence the accuracy is 78.57%.

Vehicle tracking and counting in images is a diffcult task

and produce an error result. It does not give proper count

because background image is fixed and the timing of images

taken varies. But from the video we get 100% accuracy

because here we do background subtraction..

REFERENCE

[1]https://data.gov.in/catalog/total-number-registered-motor-

vehicles-india

[2]https://en.wikipedia.org/wiki/Three-phase_traffic_theory

[3]S.Bhadra, A. Kundu and S. K. Guha, ―An Agent based

Efficient Traffic Framework using Fuzzy, Fourth

International Conference on Advanced Computing &

Communication Technologies, 2014.

[4]R.tina, G.Sharmila Sujatha- Density Based Traffic

Signal System Volume No: 2 (2015), Issue No: 9

,September 2015

[5]Farheena Shaikh - An Approach towards Traffic

Management System using Density Calculation and

Emergency Vehicle Alert International Conference on Advances in Engineering & Technology – 2014 (ICAET-

2014)

[6]Pezhman Niksaz -Automatic Traffic Estimation Using

Image Processing , Science &Research Branch, Azad

University of Yazd, 2012 International Conference on

Image, Vision and Computing (ICIVC 2012)

[7]Vivek, Tyagi, Senior Member IEEE, Shivakumar

Kalyanaraman, Fellow, IEEE, and Raghuram

Krishnapuram, Fellow, IEEE “Vehicular Traffic Density State Estimation Based On Cumulative Road

Acoustics” in IEEE Transaction on Intelligent

Transportation System.Vol.23. No.3 September 2012.

[8]Milos Borenovic, Alexender Neskovic, Natasa

Nescovic,”Vehicle positioning using gsm and cascade

connected ann structure”,IEEE transaction on intelligent

transportation system volume 14 No.1 March 2013

[9]Hasan Omar Al-Sakran “Intelligent traffic

information system based on integration of Internet of

Things and Agent technology”, IJACSA ,vol 6, 2015.

AUTHOR NAME

ROOPADEVI G HOSUR

M.TECH, DIGITAL ELECTRONICS

BITM, BELLARY

HEMANTHAKUMAR.K ASS.PROF ELECTRONICS AND

COMMUNICATIONS

BITM, BELLARY

density based traffic management using dip -...

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