An approach for moving object tracking in image processing methods

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Volume 3, Special Issue 1, ICSTSD 2016

113

An approach for moving object tracking in

image processing methods

Miss. N.S.Bharti Dr.S.N.Kale Dr. V. M. Thakare

SGBAU, Amravati SGBAU, Amravati SGBAU, Amravati

India. India. India.

[email protected]

ABSTRACT: Image processing is processing of images using mathematical operations by using any form of signal processing for which the input is an image, a series of images, or a video ,such as photograph or video frame, the output of image processing may be either an image or a set of characteristics or parameters related to the image. This paper focus on moving object tracking method, this method is used for describe a moving object in a tracking algorithm. Color, motion information, edge and texture are common features to represent moving objects. Moving object detection method, this method is used to extract moving object in video image sequences. Detecting the moving and sounding objects via utilization of canonical correlation analysis (CCA) method, it is utilized to identify the moving objects which are most correlated to the audio signal. The search & detection of moving objects method is used for addresses the detection of moving objects that could interfere with driver behavior, either through distraction or by posing an actual danger. Digital image correlation (DIC) method using fisheye lens, this method is used to inspect the interior wall displacement and strain of hollow cylinder, the fisheye lens provides a long depth of field and wide angle of view that make it suitable for this work. The proposed method can give better performance in result.

Keywords:Moving object tracking,canonical correlation analysis, Digital image correlation, Moving object

detection, Driver behavior, fisheye lens.

I. INTRODUCTION

Moving objects tracking is one of the important tasks

in computer vision. It is used widely in visual

surveillance, intelligent transport systems, industrial

vision etc. it is one of the key techniques in intelligent

video surveillance. It is an important aspect to

describe a moving object in a tracking algorithm.

Moving object detection method, this method is used

to extract moving object in video image sequences.it

is one of the basic step of target tracking, target

classification and behavior understanding. The search

& detection of moving objects in an image sequences

using a moving camera, this method addresses the

detection of moving objects that could interfere with

driver behavior, either through distraction or by

posing an actual danger. The scene is recorded as

seen by the driver, i.e., with a moving camera. Digital

image correlation (DIC) method using fisheye lens,

this method is used to inspect the interior wall

displacement and strain of hollow cylinder, the

fisheye lens provides a long depth of field and wide

angle of view that make it suitable for this work.

II. BACKGROUND

The study on image Processing discusses the most

relevant moving object tracking techniques developed

in recent years. Moving object tracking is one of the

important task in computer vision, it is used in visual

vision etc. this method is used for describe a moving

object in a tracking algorithm. Color, motion

information, edge and texture are common features to

represent moving objects[1].Moving object detection

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114 in video image sequences.it is one of the basic step of

target tracking, target classification and behavior

understanding [2].Detecting the moving and sounding

objects via utilization of canonical correlation

analysis (CCA) method, it is utilized to identify the

moving objects which are most correlated to the

audio signal. In addition to moving-sounding object

identification, the same framework is also exploited

to solve the problem of audio-video synchronization,

and is used to aid interactive segmentation [3].The

search & detection of moving objects in an image

sequence using a moving camera method, this

method interfere with driver behavior, either through

distraction or by posing an actual danger. The scene is

recorded as seen by the driver, i.e., with a moving

camera the camera is installed inside a conventional

vehicle driving on public roads. The moving camera

and the variable natural lighting pose a serious

challenge for calculating optical flow [4].Digital

image correlation method using fisheye lens method;

full-field optical measuring is increasingly being

popular measurement tools, such as digital image

correlation (DIC) method. DIC method is an optical

metrology that utilizes sub-pixel registration

algorithms for accurate measurement of full-field

deformation [5].

This paper introduces five methods for image

processing i.e. these are organizes as follows. Section

I Introduction. Section II discusses Background.

Section III discusses previous work. Section IV

discusses existing methodologies. Section V

discusses attributes and parameters and how these are

affected on images. Section VI proposed method and

outcome result possible. Finally section VII

Conclude this review paper.

III. PREVIOUS WORK DONE

In research literature, to improved moving object

detection, increase efficiency using recent techniques

[1][2][3][4][5].Moving object tracking method,

Moving objects tracking is often one of the important

tasks in computer vision. It is used widely in visual

vision etc.Color features are most widely used. But

when an object and its corresponding background

have similar color or the illumination varies rapidly,

the tracking accuracy is not ideal, Guo-wu YUAN

and Yun GAO, et al (2011) [1]. Moving objects

detection method is used to extract moving objects in

video image sequences. It's a basic step of target

tracking, target classification and behavior

understanding. Many researchers have been studying

about this issue and many algorithms of moving

objects detection are proposed, such as Background

Subtraction [1], Frame difference [2] and so on.

Either Background subtraction or Frame difference

method is hindered by the illumination change in the

scene, Chunsheng GUO and Feng XUAN,et

al(2011)[2]. Detecting the moving & sounding object

via utilization of canonical correlation analysis

(CCA) method, the canonical correlation analysis

(CCA) is utilized to identify the moving objects

which are most correlated to the audio signal, Hamid

Izadinia and Imran Saleemi,et al(2013)[3].The search

& detection of moving objects in an image sequence

using a moving camera method, the detection of

moving objects that interfere with driver behavior,

either through distraction or by posing an actual

danger. The scene is recorded as seen by the driver,

i.e., with a moving camera.The camera is installed

inside a conventional vehicle driving on public roads.

The moving camera and the variable natural lighting

pose a serious challenge for calculating optical flow,

Antonio Garcia-Dopico and José Luis Pedraza,et al

(2014)[4]. Digital image correlation method using

fisheye lens, this method is used to inspect the

interior wall displacement and strain of hollow

cylinder by digital image correlation (DIC) method

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115 depth of field and wide angle of view that make it

suitable for work,Wei-Chung Wang and Chi-Hung

Hwang,et al(2014)[5].

IV. EXISTING METHODOLOGY

Many image processing methods has been

implemented over the last several decades. There are

different methodologies that are implemented for

image processing, i.e. moving object tracking method.

Moving object detection method. Moving object

tracking method [1] this method is one of the

important task in computer vision. It is an important

aspect to describe a moving object in a tracking

algorithm.it is used in visual surveillance, intelligent

transport systems, industrial vision etc. this method is

used for describe a moving object in a tracking

algorithm. Moving object detection method [2] in

video moving objects detection, the same

illumination and perspective, will lead to that moving

objects and background is nonlinearly mixed. In

which Kernel Independent Component Analysis

(KICA) algorithm is proposed to detect the video

moving objects. Detecting the moving & sounding

object via utilization of canonical correlation analysis

(CCA) method [3] This method exploits correlation

between audio-visual dynamics of a video to segment

and localize objects that are the dominant source of

audio. The canonical correlation analysis (CCA) is

utilized to identify the moving objects which are most

correlated to the audio signal .The search & detection

of moving objects in an image sequence using a

moving camera method [4] this method addresses the

detection of moving objects that could interfere with

driver behavior, either through distraction or by

seen by the driver, i.e., with a moving camera. The

camera is installed inside a conventional vehicle

driving on public roads.Digital image correlation

method using fisheye lens method [5] this method is

used to inspect the interior wall displacement and

strain of hollow cylinder by digital image correlation

(DIC) method using fisheye lens.

V. ANALYSIS AND DISCUSSION

Moving objects tracking is often one of the important

tasks in computer vision. It is used widely in visual

vision etc. How to describe moving objects is a key

issue in a tracking algorithm. Color, motion

information, edge and texture are common features to

represent moving objects. Color features are most

widely used [1]. Moving objects detection method is

used to extract moving objects in video image

sequences. It's a basic step of target tracking, target

classification and behavior understanding

[2].Detecting the moving & sounding object via

utilization of canonical correlation analysis (CCA)

method, the canonical correlation analysis (CCA) is

utilized to identify the moving objects which are most

correlated to the audio signal [3]. The search &

detection of moving objects in an image sequence

using a moving camera method, this method interfere

with driver behavior, either through distraction or by

seen by the driver, i.e., with a moving camera [4].

Digital image correlation method using fisheye lens

method, full-field optical measuring is increasingly

being popular measurement tools, such as digital

image correlation (DIC) method[5].

Moving object

detection

Techniques

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116 Moving object

tracking

method.

In the region with similar color between moving objects and background, LBP texture can often achieve certain effects; and in the region of lacking texture.

The shadow region, which is widespread in a video, has lower brightness than the

corresponding background region, so it will also affect the tracking accuracy.

Moving object

detection

method.

In addition to using prior information & image object information but also make full use of the complementary information of different images. This method cannot satisfy the multi-frame image sequence super resolution processing requirements, single image blind restoration has become an effective means. Detecting the moving & sounding object via utilization of canonical correlation This segmentation can be used in higher level recognition and perception systems as it can determine motion of

This method does not actually perform video segmentation; assumes

availability of actor name and script.

analysis

(CCA).

interest in the scene.

The search &

detection of

moving objects

in an image

sequence using

a moving

camera.

The trade-offs between

efficiency and accuracy in optical flow algorithms is highlighted.

The main

drawback of most of the algorithms is their high computational and memory costs.

Digital image

correlation

method using

fisheye lens.

In this method, due to the features of fisheye lens, the area can be analyzed by DIC method was significantly increased. The omnidirectional image captured by fisheye lens cannot be used by DIC method directly.

Table : comparison between Moving object tracking method., Moving object detection method.,Detecting the moving & sounding object via utilization of canonical correlation analysis (CCA),The search & detection of moving objects in an image

sequence using a moving camera,Digital image correlation method using fisheye lens.

VI. PROPOSED METHODOLOGY

Many image processing strategies have been used in

this paper propose a new method for object tracking,

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117 model and optic flow. This method uses Gaussian

mixture model (GMM) and optical flow approach for

object tracking. There are two important steps to

establish the background for model, and background

updates which separate the foreground and

background. The GMM approach consists of three

different Gaussian distributions, the average, standard

deviation and weight respectively. The advantages of

Optical Flow are quick calculations and the

disadvantage is a lack of complete object tracking.

The advantage of GMM is complete results of the

operation the disadvantage is not a complete object

tracking, GMM result of the operation complete but

disadvantages include computing for a long time with

more noise. These two methods can complement each

other and image filtering results in the successful

tracking of objects. It has variety of uses such as

video communication and compression, traffic

control, medical imaging and video editing. Optical

flow method can detect the moving object even when

the camera moves, but it needs more time for its

computational complexity, and it is very sensitive to

the noise. The motion area usually appears quite

noisy in real images and optical flow estimation

involves only local computation. So the Optical Flow

method can't detect the exact contour of the moving

object, so it can conclude that there are some

shortcomings in the traditional moving object

detection methods:

- Frame difference cannot detect the exact contour of

the moving object.

- Optical Flow method is sensitive to the noise.

GMM can be used in the context of a complex

environment while Optical Flow can be used for

quick calculation with simple background. GMM is

not a complete object tracking while Object Flow

provides complete computation tracking. The

Gaussian mixture model is a single extension of the

Gaussian probability density function. As the GMM

can approximate any smooth shape of the density

distribution, so often used in image processing in

recent years for good results. Optical flow or optic

flow is the pattern of apparent motion of objects,

surfaces, and edges in a visual scene caused by the

relative motion between an observer and the scene.

Combine the GMM with the Optical Flow method, it

can obtained the results of moving object tracking.

Combine the advantages of GMM and Optical Flow.

One of the key tasks in a tracking system is to update

the object model. In most of the tracking scenarios,

the underlying image data, the object, and the scene,

evolve over time in a temporal sequence. In such

scenarios, the assumption of a constant object or

background model over the entire sequence will lead

to an impoverished tracker which cannot handle

photometric differences and occlusions. Hence it is

essential to learn the object model and adapt

accordingly.

- Input: captured with a fixed camera containing one

or more moving objects of interest

- Processing goals: determine the image regions

where significant motion has occurred

- Combine GMM and Optical Flow algorithm.

- Foreground extraction.

- Output: an outline of the motion within the image

sequence.

- Output: an outline of the motion within the image

sequence.

Input image

Backgro und model Backgro

und update

GMM Optical

flow

Backgrou nd based detection method

Noises remove

Time gradient

image

Foreground extract

Foreground extract Math

morpho logy median

filter

Object segmentati

on

The purposed

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118 Fig: flow diagram for object tracking using

Gaussian mixture model and optical flow.

VII. OUTCOME& POSSIBLE RESULT

Combine the GMM with the Optical Flow method,

then obtained the results of moving object tracking.

Combine the advantages of GMM and Optical Flow.

One of the key tasks in a tracking system is to update

the object model.

VIII. CONCLUSION

This paper focused on the study of different moving

object detection techniques i.e. Moving object

tracking method., Moving object detection

method,Detecting the moving & sounding object via

utilization of canonical correlation analysis

(CCA),The search & detection of moving objects in

an image sequence using a moving camera,Digital

image correlation method using fisheye lens. This

paper proposed the GMM and Optical Flow method

successfully applied in a continuous image.it uses the

GMM approach as the main tracking algorithm, with

morphological and median filtering to remove noise

and also it uses the optical flow method to subtract

successive images, also using morphological and

median filters to remove noise.

IX. FUTURE SCOPE:

In future this method can be modified to differentiate

different class objects in real time video. Later

characteristics are extracted and applied to a Neural

Network so that segmented objects are classified as

vehicles and non-vehicles and, in the case of vehicles,

they will be classified according to the size of the

vehicle as follows: large size, intermediate size, small

size.

REFERENCES

[1] Guo-wu YUAN and Yun GAO, "A Moving Objects Tracking Method Based On a Combination of Local Binary Pattern Texture and Hue ,"sciencedirect, PP. 3964-3968, 2011.

[2] ChunshengGUO and FengXUAN,"Moving object detection based on kernel independent component analysis,"sciencedirect,PP.1046-1050,2011.

[3] Hamid Izadinia and Imran Saleemi,"Multimodal Analysis for

Identification andSegmentation of Moving-Sounding Objects,"IEEE TRANSACTIONS ON MULTIMEDIA, Vol: 15, No. 2, PP.378-390, FEBRUARY 2013

[4] Antonio Garcia-Dopicoand José Luis Pedraza,"Locating moving objects in car-drivingsequences,"Journal,PP.1-23, January 2014.

[5] Wei-Chung Wang and Chi-Hung Hwang,"Displacement Measurement of Interior Wall of Hollow Cylinder by Digital Image Correlation Method Using Fisheye Lens ,"sciencedirect, PP.437-446,2014. Output