A PROJECT REPORT ON MOVING OBJECT DETECTION FROM VIDEO ... · MOVING OBJECT DETECTION FROM VIDEO USING NEURAL NETWORK Submitted in partial fulfillment for the requirement of the award

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A PROJECT REPORT

ON

MOVING OBJECT DETECTION FROM VIDEO USING

NEURAL NETWORK

Submitted in partial fulfillment for the requirement of the award of

DEGREE

IN

MASTER OF SCIENCE IN COMPUTER SCIENCE

Paper Code:- MCS 1005

Submitted by

Shantasree Kar

Roll: 101611 No.:02220123

Regn No.: 22-110021754 of 2011-2012

Under the supervision of

Dr.Prodipto Das,

Asst. Professor

Department of Computer Science

Assam University , Silchar

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Date: ………………….

CERTIFICATE

This is to certify that the project paper work entitled “MOVING

OBJECT DETECTION FROM VIDEO USING NEURAL

NETWORK” submitted by Shantasree Kar(Roll: 101611 No.:

02220123), hereby recommended to be accepted for the partial

fulfillment of the requirements for M.Sc(Computer Science) degree

from Assam University.

( Dr.Bipul Syam Purkayastha ) Head of the Department

Department of Computer Science

Assam University, Silchar

Pin – 788011

DEPARTMENT OF COMPUTER SCIENCE

SCHOOL OF PHYSICAL SCIENCES

ASSAM UNIVERSITY SILCHAR

A CENTRAL UNIVERSITY CONSTITUTED UNDER ACT XIII OF 1989

ASSAM, INDIA, PIN - 788011

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Date: ………………….

CERTIFICATE

This is to certify that the project paper work entitled “MOVING

OBJECT DETECTION FROM VIDEO USING NEURAL

NETWORK” submitted by Shnatasree Kar(Roll: 101611 No.:

02220123), hereby recommended to be accepted for the partial

fulfillment of the requirements for M.Sc(Computer Science) degree

from Assam University.

(Dr.Prodipto Das)

Assistant Professor

Department of Computer Science

Assam University, Silchar

Pin – 788011

DEPARTMENT OF COMPUTER SCIENCE

SCHOOL OF PHYSICAL SCIENCES

ASSAM UNIVERSITY SILCHAR

A CENTRAL UNIVERSITY CONSTITUTED UNDER ACT XIII OF 1989

ASSAM, INDIA, PIN - 788011

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DECLARATION

I, SHANTASREE KAR do hereby declare that the project paper work

entitled “MOVING OBJECT DETECTION FROM VIDEO USING

NEURAL NETWORK” has been carried out by me under the guidance of

Dr.Prodipto Das, Assistant Professor, Department Of Computer Science,

Assam University, Silchar. Whenever I have used materials (data,

theoretical analysis, figures, and text) from other sources, I have given due

credit to them by citing them in the text of this report and giving their

details in the references.

Date…………………. Shantasree Kar

Roll :-101611 No.: 02220123

Regn No.:-22110021754

Department of Computer Science

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ACKNOWLEDGEMENT

My sincere gratitude and thanks towards my project paper guide Dr.Prodipto

Das, Assistant Professor, Department of Computer Science, Assam University,

Silchar, Assam,India.

It was only with his backing and support that I could complete the report. He

provided me all sorts of help and corrected me if ever seemed to make mistakes.

I have no such words to express my gratitude.

I acknowledge my sincere gratitude to the HOD of Computer Science

Department, Assam University, Silchar. He gave me the permission to do the

project work. Without his support I couldn’t even start the work. So I am

grateful to him.

I acknowledge my sincere gratitude to the lecturers, research scholars and the

lab technicians for their valuable guidance and helping attitude even in their

very busy schedule.

And at last but not the least, I acknowledge my dearest parents for being such a

nice source of encouragement and moral support that helped me tremendously

in this aspect.

I also declare to the best of my knowledge and belief that the Project Work has

not been submitted anywhere else.

Place: SHANTASREE KAR

Date: Roll : 101611 No.:02220123

Department of Computer Science

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Chapter1

INTRODUCTION

Moving object detection is a computer technology related to computer vision, image

processing, neural network that deals with detecting instances of semantic objects of a

certain class ( such as human, cars etc) in digital image or video. Well researched

domains include vehicle detection, pedestrian detection.Moving object detection has

many applications in the domain of computer vision, including image retrieval and

video surveillance.

Moving object detection for real world applications is still a challenging problem.

While recent research datasets increases the amount of training sets and testing

examples to get closer to the real world problems, the ability of detectors to process

large data sets in reasonable time becomes another important issue besides accuracy.

It is not only the training examples that matters, but also the number of classes.

Moving object detection involves locating objects in the frame of a video sequence.

Every tracking method requires an object detection mechanism either in every frame

or when the object first appears in the video. In moving object detection various

background subtraction techniques available in the literature were simulated.

Background subtraction involves the absolute difference between the current image

and the reference updated background over a period of time. A good background

subtraction should be able to overcome the problem of varying illumination condition,

background clutter, shadows, camouflage, bootstrapping and at the same time motion

segmentation of foreground object should be done at the real time.

The moving object tracking in video pictures has attracted a great deal of interest in

computer vision. Object tracking is the first step in surveillance systems, navigation

systems and object recognition. There is a huge significance of object tracking in real

time environment as it enables several important applications such as to provide better

sense of security using visual information, Security and surveillance to recognize

people, to analyze shopping behaviour of customers in retail space, video abstraction

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to obtain automatic annotation of videos, to generate object based summaries, traffic

management to analyze flow, to detect accidents, video editing to eliminate

cumbersome human operator interaction, to design futuristic video effects.

NEURAL NETWORK

A neural network is a massively parallel distributed processor made up of a single

processing units , which has a natural propensity for storing experiential knowledge

and making it available for use. It resembles the brain in two aspects:

1. Knowledge is acquired by the network from its environment through a learning

process.

2. Interneuron connection strengths, known as synaptic weights, are used to store

the acquired knowledge.

The procedure used to perform the learning process is called a learning

algorithm , the function of which is to modify the synaptic weights of the

network in an orderly fashion to attain a desired design objective.

The modification of synaptic weights provides the traditional methods for the

design of neural network. However, it is possible for a neural network to

modify its own topology which is motivated by the fact that neurons in the

human brain can die and that new synaptic connection can grow.

Neural networks are also referred to in literature as neurocomputers ,

connectionist networks , parallel distributed processors etc.

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ADVANTAGE OF NEURAL NETWORK

Neural networks are an approach to computing that involves developing mathematical

structures with the ability to learn. Neural network have the remarkable ability to

derive meaning from complicated or imprecise data and can be used to extract pattern

and detect trends that are too complex to be noticed by either human or other

computer techniques. A trained neural network can be thought as an expert in the

category of information it has been given to analyze.

Neural networks have broad applicabilities to real world business problem and have

already been successfully applied in many industries.

Neural networks use a set of processing elements analogous to neurons in the brain.

These processing elements are interconnected in a network that can identify patterns

in data. These distinguishes neural network from other computing programs that

simply follow instructions in a fixed sequential order.

The structure of neural network look something like the following:-

Fig1:- Interconnected Neural Network.

Here the first layer is the input layer, second layer is the hidden layer and the third

layer is the output layer.

Each node in the hidden layer is fully connected to the inputs which mean that what is

learned in a hidden node is based on all the inputs taken together. Statisticians

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maintain that the network can pick up the interdependencies in the model. The

following diagram gives an idea of what goes inside the hidden layer

Fig2:- Interconnection weights of neural network.

The weighted sum is performed: X1*W1+X2*W2+…+Xn*Wn.

This weighted sum is performed for each hidden layer and that is how

interconnection is represented.

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Chapter-2

FUNDAMENTALS OF NEURAL NETWORK

There are many different types of neural networks, but they all have four basic

attributes:-

A set of processing units;

A set of connections;

A computing procedures;

A training procedure.

Processing Units:

A neural network contains a potentially huge number of very simple processing units .

All these units operate simultaneously, supporting massive parallelism. All

computation in the system is performed by these units. At each moment in time each

unit simply computes scalar function of its local inputs and broadcasts the result called

the activation value to its neighbouring units. The units are typically divided into input

units, which receive data from the environment, hidden units, which may internally

transform the data representation, and/or output units, which represent decisions

control or decisions signals.

Connections:-

The units in a neural network are organized in a given topology by a set of

connections or weights. Each weight has a real value typically ranging from -∞ to +∞,

although sometimes range is limited. The value or weight of a neuron describes how

much influence does a neuron have on its neighbour. The values of all the weights

predetermine the network’s computational reaction to any arbitrary input pattern.

Weights can change as a result of training, but they tend to change slowly.

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A network can be connected with any kind of topology. Common topologies include

unstructured, layered, modular and recurrent.

Computation:-

Computation always begins by presenting an input pattern to the network. Then the

activations of all the remaining units are computed.A given unit is typically updated in

two stages: first we compute the unit’s net input and then we compute its output

activation.

In the standard case, the net input Xj for unit j is just the weighted sum of the inputs

Xj=∑YiWji

Where Yi is the output activation of an incoming unit, and Wij is the weight from unit i

to unit j.

Once we have computed the unit’s net input Xj, we compute the output activation Yi

as a function of Xj. .This activation function also called a transfer function can be

either deterministic or stochastic. Deterministic local activation usually take one of the

three forms- linear, threshold or sigmoidal as shown in figure.

(a) (b) (c)

Fig3:-Deterministiclocal activation function:(a) linear (b)threshold (c) sigmoidal

The simplest form of non-linearity is provided by the threshold activation function:-

y = o if X<=0

=1 if X>0

Training:-

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Training of a network, in the most general sense means adapting the connections so

that the network exhibits the desired computational behaviour for all input patterns.

The process usually involves modifying the weights but sometimes it also involves

modifying the actual topology of the network. Topological changes can improve both

generalization and the speed of learning, by constraining the class of function that the

network is capable of learning.

TAXANOMY OF NEURAL NETWORK

There are three main classes of learning procedures:-

Supervised learning, in which a teacher provides output target for each input

pattern and corrects the networks errors explicitly.

Semi-supervised(or reinforcement) learning, in which a teacher merely

indicates whether the network’s response to a training pattern is good or bad.

Unsupervised learning, in which there is no teacher, and the network must find

regularities in the training data by itself.

Supervised learning

Perceptrons are the simplest types of feedforward networks that use supervised

learning. In case of single layer perceptron, the Delta rule can be applied directly.

Beacause a perceptron’s activation are binary, this general learning rule reduces to the

perceptron learning rule, which says that if an input is active(Yi=1) and the output Yj

is wrong then Wij should be either increased or decreased by a small amont of e,

depending if the desired output is 1 or 0, respectively.

Multilayer perceptron(MLPs) can theoretically learn any function, but they are more

complex to train. The delta rule cannot be applied directly to MLPs because thereare

no targets in the hidden layers. However if an MLP uses continuous rather than

distinct activation functions i.e, sigmoids rather than threshold functions, then it

becomes possible to use partial derivatives and chain rule to derive the influence of

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any weight on any output activation, which in turn indicates how to modify that

weight in order to reduce the network’s error. This generalization of the Delta Rule is

known as backpropagation.

Semi-Supervised learning

Semi-supervised learning is class of supervised learning tasks that also make use of

unlabeled data for training typically a small amount of labelled data with a large

amount of unlabeled data. The problem of semi supervised learning is reduced to the

problem of supervised learning, by setting the training targets to be either the actual

outputs or their negations, depending on whether the network’s behaviour was judged

good or bad. The network is than trained using the Delta Rule, where the targets are

compared against the network’s mean outputs, and error is backpropagated through

the network if necessary.

Unsupervised learning

In unsupervised learning there is no teacher, and a network must detect regularities in

the input data by itself. Such self-organizing networks can be used for compressing,

clustering, quantizing, classifying, or mapping input data. One way to perform

unsupervised training is to recast it into the paradigm of supervised training, by

designating an artificial target for each input pattern, and applying backpropagation.

In particular we can train a network to reconstruct the input pattern on the output

layer, while passing the data through a bottleneck of hidden units. Such a network

learns to preserve as much information as possible in the hidden layer. This type of

network is often called an encoder, especially when the inputs/outputs are binary

vectors.

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Chapter-3

LITERATURE SURVEY

1. Takaya et al.(2005):- This paper made use of the neural network to track the

motion of moving objects recorded in a sequence of video images. Given the

motion vector of an arbitrary pixel and colour, the neural network determines if

the pixel belong to the moving object or not.

2. Deng et al.(2010):- A study on vehicle and pedestrian recognition based on

back propagation (BP) neural network is presented in this paper. First, extract

the moving objects from the image sequence using background subtraction.

Second, select part of the objects for further detected. Third, extract several

significant eigen values from the rest objects, which can indicate the

differences of contour between pedestrian and vehicle. Finally, eigen vector is

formed and used as the input of the back propagation neural network, the

output of which is the detecting result.

3. Meenatchi et al(2014):- . A robust and real-time method for tracking objects is

presented in this paper. The proposed algorithm includes two stages: object

tracking, object Segmentation. According to the segmented object shape, a

predict method based on Kalman filter is proposed. Kalman filter model is

used to tracking and predicting the trace of an object. Image enhancement is the

process of adjusting tracked frame images so that the results are more suitable

for display. Edge detection technique is used for finding discontinuities in gray

level images of tracked frames. Finally segmented frames are converted into

video sequence.

4. Tiwari et al.(2012):- This paper addresses the problem of scene understanding

based on neural network and image segmentation. Here they used the

backpropagation algorithm to train the network and features are extracted

using colours in the RGB colour spaces.

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Chapter-4

SEGMENTATION

Segmentation is the process of partitioning a digital image into multiple regions or set

of pixels.

The goal of segmentation is to simplify and/or change the representation of an

image into something that is more meaningful and easier to analyze. The result of

segmentation is a set of segments that collectively cover the entire scene or a set of

contours extracted from the scene. Each of the pixel in a region is similar with respect

to some characteristics or computed property such as colour, intensity or texture.

Adjacent regions are significantly different with respect to the same characteristic(s).

SEGMENTATION OF OBJECTS IN IMAGE SEQUENCES

Images are segmented into objects to achieve efficient compression by coding the

contour and texture separately. As the purpose is to achieve high compression

performance, the objects segmented may not be semantically meaningful to human

observers. The more recent applications, such as content-based image/video retrieval

and image/video composition, require that the segmented objects be semantically

meaningful. Finding moving objects in image sequences is one of the most important

tasks in computer vision and image processing. Background subtraction approach

means to compute the stationary background image and to identify the moving objects

as those pixel in the image that differ significantly from the background. Background

subtraction can provide an effective means of locating a moving object.

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ALGORITHM OF GAUSSIAN MIXTURE MODEL:

In order to give a better understanding of the algorithm used for background

subtraction the following steps were adopted to achieve the desired results:

1. Firstly, we compare each input pixels to the mean 'µ' of the associated components.

If the value of a pixel is close enough to a chosen component's mean, then that

component is considered as the matched component. In order to be a matched

component, the difference between the pixel and mean must be less than compared to

the component's standard deviation scaled by factor D in the algorithm.

2. Secondly, update the Gaussian weight, mean and standard deviation (variance) to

reflect the new obtained pixel value. In relation to non-matched components the

weights 'w' decreases whereas the mean and standard deviation stay the same. It is

dependent upon the learning component 'p' in relation to how fast they change.

3. Thirdly, here we identify which components are parts of the background model. To

do this a threshold value is applied to the component weights 'w'.

4. Fourthly, in the final step we determine the foreground pixels. Here the pixels that

are identified as foreground don’t match with any components determined to be the

background.

Background modeling by Gaussian mixtures is a pixel based process. Let x be a

random process representing the value of a given pixel in time. A convenient

framework to model the probability density function of x is the parametric Gaussian

mixture model where the density is composed of a sum of Gaussians. Let p(x) denote

the probability density function of a Gaussian mixture comprising K component

densities:

p(x)=∑wk N(x;μk,σk) k=1 to K

where wk are the weights, and N(x;μk,σk) is the normal density of mean μk and

covariance matrix Σk = σkI,( I denotes the identity matrix).

First, the parameters are initialized with wk = w0, μk = μ0 and σk = σ0. If there is

amatch, i.e.

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||x – μj||/σj <τ for some j ∈ [1..K]

where τ(> 0) is some threshold value, then the parameters of the mixture are

updated as follows:

wk(t) = (1− α)wk(t − 1) + αMk(t),

μk(t) = (1− β)μk(t − 1) + β x,

σ2 k(t) = (1− β)σ2 k(t − 1) + β ||(x − μk(t))||2,

where Mk(t) is equal to 1 for the matching component j and 0 otherwise. If there is no

match, the component with the lowest weight wk is re-initialized with wk = w0, μk = x

and σk = σ0. The learning rate α is constant and β is defined as:

β = αN(x;μk,σk).

Finally ,the weights wk are normalized at each iteration to add up to 1.

Fig4:- Background subtraction model

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EXTRACTION OF FRAMES

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Fig:-5 Background subtraction

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TEMPORAL DIFFERENCING

In temporal differencing, moving regions are detected by taking pixel-by-pixel

difference of consecutive frames (two or three) in a video sequence. Temporal

differencing is the most common method for moving object detection in

scenarios where the camera is moving. Here the moving object is detected by taking

the difference of consecutive image frames t-1 and t.

FOREGROUND DETECTION

In this step, it identifies the pixels in the frame. Foreground detection compares the

video frame with the background model, and identify candidate foreground pixels

from the frame. Commonly- used approach for foreground detection is to check

whether the pixel is significantly different from the corresponding background

estimate.

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Chapter-5

OBJECT TRACKING

After the object detection is achieved, the problem of establishing a correspondence

between object masks in consecutive frames should arise. Obtaining the correct track

information is crucial for subsequent actions, such as object identification and activity

recognition. For this situation, Kalman filtering technique is used.The Kalman filter is

a recursive two-stage filter. At each iteration, it performs a predict step and an update

step.

The predict step predicts the current location of the moving object based on previous

observations. For instance, if an object is moving with constant acceleration, we can

predict its current location, based on its previous location, , using the equations of

motion.

The update step takes the measurement of the object’s current location (if available),

and combines this with the predicted current location, , to obtain an a posteriori

estimated current location of the object.

The equations that govern the Kalman filter are given below

1. Predict stage:

A. Predicted (a priori) state:

B. Predicted (a priori) estimate covariance:

2. Update stage:

A. Innovation or measurement residual:

Z

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C. Optimal Kalman gain:

D. Updated (a posteriori) state estimate:

E. Updated (a posteriori) estimate covariance:

They can be difficult to understand at first, so let’s first take a look at what each of

these variables are used for:

is the current state vector, as estimated by the Kalman filter, at time t .

Zt the measurement vector taken at time t .

Pt measures the estimated accuracy of at time t .

F describes how the system moves (ideally) from one state to the next, i.e. how

one state vector is projected to the next, assuming no noise (e.g. no acceleration)

Hdefines the mapping from the state vector, , to the measurement vector, .

Qand R define the Gaussian process and measurement noise,respectively, and

characterise the variance of the system.

B and U are control-input parameters are only used in systems that have an input;

these can be ignored in the case of an object tracker.

The two stages of the filter correspond to the state-space model typically used to

model linear dynamical systems. The first stage solves the process equation:

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The process noise is additive Gaussian white noise (AWGN) with zero mean and

covariance defined by:

The second one is the measurement equation:

The measurement noise V is also AGWN with zero mean and covariance defined by:

In order to implement a Kalman filter, we have to define several variables that model

the system. We have to choose the variables contained by Xt and Zt and, and also

choose suitable values for F,H,Q,R as well as an initial value for Pt.

We will define our measurement vector as:

where and are

the upper-left and lower-right corners of the bounding box around the detected object,

respectively.

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Chapter-6

BLOB ANALYSIS

For image processing, a blob is defined as a region of connected pixels. Blob analysis

is the identification and study of these regions in an image. The algorithms discern

pixels by their value and place them in one of two categories: the foreground

(typically pixels with a nonzero value) or the background (pixels with a zero value).

Blob analysis is used in finding blobs whose spatial characteristics satisfy certain

criteria. In many applications where computation is time consuming, blob analysis is

used to eliminate blobs that are of no interest based on their spatial characteristics, and

keep only the relevant blobs for further analysis. It can also be used to find statistical

information such as the size of the blobs or the number, location, and the presence of

blob regions.

Another typical problem of any motion detection system is its reliability in the

presence of sudden changes in light conditions. This is typical of indoor environments

(owing to light switches) but also of outdoor contexts, owing, for example, to a

sudden cloud. In such cases, the system should be able to re-establish normal

conditions as soon as possible. This kind of situation can be easily detected by

monitoring continuously the variations between consecutive images of moving points.

The difference between the percentages of moving points in Itm and It-1

mhas been

evaluated as follows:

Dt =||Num(Itm )-Num(It-1

m)|| / dim(I)

where Num() is the number of black points (i.e. moving points)

in the image and dim() is the image dimension.

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Fig6:- Background subtraction of new position

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Chapter-7

TRAINING OF NEURAL NETWORK

BACKPROPAGATION

The term “backpropagation” has come from the word backward propagation of

error. It follows supervised learning method. It is a method of training artificial

neural networks used in conjunction with an optimization method such as gradient

descent. Backpropagation requires that the activation function used by the artificial

neurons be differentiable. The backpropagation learning algorithm has two

phases : Propagation and weight update.

Propagation

Each propagation involves the following steps:

1. Forward propagation of a training pattern’s input through the neural network in

order to generate the propagation's output activations.

2. Backward propagation of the propagation’s output activations through the

neural network using the training pattern's target in order to generate the deltas of

all output and hidden neurons.

Weight update

For each weight synapse follow the following steps:

1. Multiply its output delta and input activation to get the gradient of the

weight.

2. Bring the weight in the opposite direction of the gradient by subtracting a

ratio of it from the weight.

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This ratio influences the speed and quality of learning; it is called the learning rate.

The sign of the gradient of a weight indicates where the error is increasing, this is

why the weight must be updated in the opposite direction.

Repeat the two phases until the performance of the network is satisfactory.

BACKPROPAGATION ALGORITHM

The error signal at the output of neuron j at iteration n (i.e, presentation of the nth

training example) is defined by

ej(n) = dj(n) – yj(n), neuron j at output node 1.1

We define the instantaneous value of the energy of neuron j as 1/2ej2(n),

correspondingly the instantaneous value Ɛ(n) of the total error energy is obtained by

summing 1/2ej2(n) over all neurons in the output layer.

Ɛ(n) = 1/2∑ej2(n) [j c] 1.2

Where the set c includes all the neurons in the output layer of the network . Let N

denote the total number of patterns contained in the training set. The average squared

error energy is obtained by summing Ɛ(n)over all n and then normalizing with respect

to the size N, given by

Ɛav= 1/N∑Ɛ(n) [n = 1to N] 1.3

The instantaneous error energy Ɛ(n) and therefore the average error energy Ɛav is a

function of all the free parameters of the network. For a given training set Ɛav

represents the cost function as a measure of learning performance. The objective of

the learning process is to adjust the free parameters of the network to minimize Ɛav.

The arithmetic average of these individual weight changes over the training set is

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therefore an estimate of the true change that would result from modifying the weights

based on minimizing the cost function Ɛav over the training set

Fig7: Signal flow graph highlighting the details of output neuron j.

Consider the fig above which depicts neuron j being fed by a set of function signals

produced by a layer of neurons to its left. The individual local field vj(n) produced at

input layer of the activation function associated with neuron j is therefore

Vj(n) = ∑wji(n)yi(n) [ i = 0 to m ] 1.4

Where m is the total number of inputs excluding the bias applied to neuron j . Hence

the function signal yj(n) appearing at the output of neuron j at iteration n is

Yj(n) = φj(vj(n)) 1.5

The back-propagation algorithm applies a correction ∆wji(n) to the synaptic weight

wji(n), which is proportional to the partial derivative ∂Ɛ(n) / ∂wji(n).

According to the chain rule of calculus we may express the gradient as :

∂Ɛ(n) /∂wji(n) =[∂Ɛ(n)/∂ej(n)][ ∂ej(n)/∂yj(n)][ ∂yj(n)/∂vj(n)][ ∂vj(n)/ ∂wji(n)] 1.6

Differentiating both sides of Eq. 1.2 w.r.t ej(n), we get

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∂Ɛ(n)/ ∂ej(n) = ej(n) 1.7

Differentiating Eq. 1.1 w.r.t yj(n) we get

∂ej(n)/∂ yj(n) = -1 1.8

Differentiating Eq. 1.5 w.r.t vj(n) we get

∂ yj(n)/∂vj(n) = φj'(vj(n)) 1.9

Differentiating Eq. 1.4 w.r.t wij(n) we get

∂vj(n)/∂ wij(n) = yn 1.10

The use of equations 1.7 to 1.10 in 1.6 yields

∂Ɛ(n) /∂wji(n) = -ej(n) φj'(vj(n))yi(n) 1.11

The correlation ∆wji(n) applied to wji(n) is defined by the delta rule

∆ wji(n) = -η ∂Ɛ(n) / ∂ wij(n) 1.12

Where η is the learning rate parameter of the backpropagation algorithm.

Accordingly the use of eq. 1.11 and 1.12 yields

∆ wji(n) = -ηδj(n)yi(n) 1. 13

Where δ is the local gradient , defined by

δj(n) = ∂Ɛ(n)/∂vj(n)

= - [∂Ɛ(n)/∂ej(n)][ ∂ej(n)/∂yj(n)][ ∂yj(n)/∂vj(n)]

= ej(n) φj'(vj(n)) 1.14

If neuron j is hidden we can write ,

∂j(n) = φj'(vj(n))∑∂k(n)wkj(n) 1.15

Summarizing the back propagation algorithm we can write[3]

[weight correction ] = [ learning parameter][local gradient ][inputsignal]

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Fig8: Moving object detected

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Fig9: Moving object detected

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Fig:-10 Training of the network

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Chapter 8

DEVELOPMENT PLATFORMS

SOFTWARE:

The given system is developed on MATLAB 2013a

MATLAB(matrix laboratory) is a numerical computing environment snd fourth

generation programming language. Developed by MathWorks, MATLAB allows

matrix manipulations, plotting of functions and data, implementation of algorithms,

creation of user interfaces, and interfacing with programs written in other languages

including C, C++,Java and Fortran.

Although MATLAB is intended primarily for numerical computing, an optional

toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing

capabilities. An additional package, Simulink, adds graphical multi-domain

simulation and model-basd design for dynamic and embedded systems.

MATLAB is a programming environment for algorithm development, data analysis,

visualization and numerical computation. Using MATLAB we can solve technical

computing problems faster than the traditional programming languages such as C,

C++ and Fortran. We can use MATLAB in a wide range of applications including

signal and image processing, communications, control design, test and measurement.

For a million of engineers and scientists in industry and academia, MATLAB is the

language of technical computing. The MATLAB application is built around the

MATLAB language and most use of MATLAB includes Command Window or

executing textfiles containing MATLAB code and functions.

Page | 35

OPERATING SYSTEM

WINDOWS 8(32bit)

HARDWARE:

PROCESSOR: CPU(Intel N3540 2.16G)

RAM: 4.00GB (3.87GB usable)

SYSTEM TYPE: 32bit operating system

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Chapter 9

CONCLUSION AND SCOPE FOR FUTURE WORK

CONCLUSION

In this work the GMM method is successfully applied in a continuous image.

We used the GMM approach as the main tracking algorithm, with morphological and

median filtering to remove noise. The success of the foreground and background

segmentation and found the object coordinates. We have trained the network with

backpropagation neural network as neural network are well known for their ability to

learn complex functions , generalize effectively, tolerate noise and support

parallelism.

SCOPE FOR FUTURE WORK

No software can ever demand to be completely self complete and independent

without requiring any future modification. The problem of moving object detection is

very broad field and many problems are yet to be solved. Further development of our

project includes training the network to detect large number of object which also

include moving leaves ,shadow which should be avoided and should not be considered

as moving objects.

Page | 37

REFERENCES

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in Video Sequences” International Journal of Advance Research in Computer

Science and Management Studies, Volume 2, Issue 5, May 2014

2. VPS Naidu and J.R. Rao, “Object Tracking using Image Registration and

Kalman Filter”.

3. Mr. D. W. Chinchkhede and Mr. N. J. Uke, “Image segmentation in video

sequences using Modified Background Subtraction,” International Journal of

Computer Science & Information Technology (IJCSIT) Vol 4, No 1, Feb 2012.

4. P. Spagnolo, T.D’ Orazio and M. Leo, A. Distante, “Moving object

segmentation by background subtraction and temporal analysis,”Istituto di

Studi sui Sistemi Intelligenti per l’Automazione—CNR, Via Amendola 122/D-

I, 70126 Bari, Italy.

5. P. Wayne Power and Johann A. Schoonees, “Understanding Background

Mixture Models for Foreground Segmentation,” Proceedings Image and Vision

Computing New Zealand 2002 University of Auckland, Auckland, New

Zealand 26–28th November 2002.

6. Abdesselam Bouzerdoum , Azeddine Beghdadi and Philippe L. Bouttefroy ,

“On the analysis of background subtraction techniques using Gaussian mixture

models,” 2010 IEEE International Conference on Acoustics, Speech, and

Signal Processing (pp. 4042-4045). USA: IEEE.

7. Abhishek Kumar Chauhan and Prashant Krishan , “Moving Object Tracking

using Gaussian Mixture Model and Optical Flow,” International Journal of

Advanced Research in Computer Science and Software Engineering ,

Volume 3, Issue 4, April 2013.

8. Arti Tiwari & Jagvir Verma, “Scene understanding using back propagation by

neural network ‘’International Journal of Image Processing and Vision

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9. Simon Haykin,Neural Networks: A Comprehesive Foundation,Prentice

Hall,1999.

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10. Thierry Bouwmans, Fida El Baf, Bertrand Vachon, “Background Modeling

using Mixture of Gaussians for Foreground Detection - A Survey”.