Moving Object Detection Based on Multiple Quadrant Histogram Ver1a

8/13/2019 Moving Object Detection Based on Multiple Quadrant Histogram Ver1a

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Contents

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Introduction Moving Object Detection

Project Abstract

Development and Target System

Reference Paper Detection Algorithm

Project Flow chart

Development System

Event Trigger

Extracting Object Information

Applications

Future Work

Conclusions


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Introduction Moving Object Detection

Why Moving Object Detection

The core of security and business intelligence surveillance video application is the Moving

Object Detection. The information derived from the process can be used to alert security

camera monitoring staff to potential trespass or rule violation in the sensitive areas. In case of

business intelligence the information can be used to gather positive statistical information

relating to customer behavior.

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Project Abstract

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Problem Statement - Low Processing Power, Background less Moving Object Detection

As the complexity of the video analytics processing increases in embedded system, more will be theusage of efficient methods to detect the moving objects. Most of the video surveillance method use background model and the correctness of the surveillance depends on the background and adaptiveupdate to background. However this method take sufficient processing bandwidth and embedding into low cost solution becomes not feasible.

Alternately Histogram based detection is fairly efficient method to detect moving object. This method

can be combined with multiple quadrant event trigger to quickly detect object.

So this project aims to detect the moving object using technique which does not require backgroundmodel and derive a effective mechanism which can be embedded in ARM Board

Development Platform: MATLAB

Target Platform : TI ARM Processor

OS : Linux

Activity : Image processing Algorithm

Application : Video Surveillance


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Development and Target System

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Camera PC with MATLABUSB

Image acquisition

Algorithm development

Camera ARM ProcessorUSB

SDRAM NAND Flash

RS232 JTAG

Ethernet

PCwith MATLAB

Microsoft Webcam

30 frames/minute

Development System Target System

Hardware Platform


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Reference Paper Detection Algorithm

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Based on single Gaussian Distribution The color of every pixels are shown by Gaussian

distribution model Color and covariance differ, then pixel is

considered as foreground pixel After foreground detection, use average value and

covariance matrix of Gaussian to update Use inter-frame difference method and find the

difference between two adjacent frames in video

sequence Update background model Obtain moving object by processing difference

between current frame and updated background

model


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Project Flow chart

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PAD/Quadrant

FourQuadrant


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Project Flow chart

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Development System

Development System

Beagle Board - xM

LaptopMATLABWebcam

Status

S.No Activities Status

1 MATLAB Setup Completed

2 Study of Image ProcessingCommands

Completed

3 Image Acquisition using MATLABImage Acquisition Tool Box

Completed

4 Displaying Video Frames in MATLAB Completed

Output

File Traffic.avi

Path C:\Users\srikanthm\Desktop

Frame Width andheight

160 &120

No of Frames 61

The implementation of the moving object identification is planned in two phases. In Phase 1, themoving object detection algorithm is developed in MATLAB.In Phase 2, this algorithm is ported totarget Platform.


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Event TriggerThe implementation of the Event Trigger is done using MATLAB Program. The detector is a Padwhich covers an image region with dimensions 40x40 with starting Co-ordinate 40x60

The first step is construction of the threshold pad temporal difference image Dt

WherePt Pad image at time t

- Pixel Difference Threshold

For the traffic.avi file, the pad temporal difference image is constructed between frame1 and frame 61where the difference exists

The mean difference across the pad is calculated

Where N- is the number of pixels in the pad imagedt- mean differences threshold

The trigger occurs when rises through a mean difference and the mean differences areincreasing.


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Extracting Object Information

In absence of a trigger, a normalized 8 bin probably background grey scale histogram of the padimage at time t. The histogram is used to recursively update a time averaged probably background histogram as given below

Where

- pad image histogram typically of the order 0.2

Frame 1

t0

Frame 2

t1

Frame 31

t30

Frame 32

t31

Frame 60

t59

Frame 61

t60

PadObject

Event Trigger

AverageProbably backgroundHistogram


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Difference Histogram

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Frame 1t0

Frame 2t1

Frame 31t30

Frame 32t31

Frame 60t59

Frame 61t60

Pad

Object

Event Trigger

AverageProbably backgroundHistogram

Difference


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Difference Masked Pad Image Mp

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Frame 1t0

Frame 2t1

Frame 31t30

Frame 32t31

Frame 60t59

Frame 61t60

PadObjectPt(x,y)

-

Dt(x,y)

x

Mp(x,y)

This contains objects, shadows, ghosts and zero values associated with static region


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Difference Masked Pad Image Mp

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Mp(x,y)

U=1

U=2

U=8

Dirac function ofCompare gray value

with bin boundary


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U=1

U=2

U=8

U=1

U=2

U=8

+ + + +


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Applications

Video Surveillance

No processing time waste in updating background Reality and Visual Effects

Medical Imaging

the security monitoring domain

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Future Work

A multiple pad can make a tripwire type arrangement which can be

effectively used for Tracker initialization. The existing method can beenhanced with additional tracker initialization and be used effectively insecurity alerts.

This embedded camera system may include short range wireless alertmessage features which can alert people around radius of 100m. Thefuture scope includes making a independent, smart video analytics deviceembedded in to camera itself which has facility to communicate tosurrounding intelligent devices too.

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Conclusions

We have presented a computationally economic approach to detection of

event as well as object detection and identification. It is one of a numberof approaches developed to use spare processing capacity for embeddedanalytics in intelligent cameras. This method significant differs from othermethods by efficient way of event trigger and histogram based detection.

It has potential for development in contexts such as Railway Platformdensity indication, or as a object identification in situations such as remotevideo surveillance. The method does not demand the development of afull background image or classifier training. It works with moderatequality monochrome footage and can be used in a range of contexts .

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Thank You

Moving Object Detection Based on Multiple Quadrant Histogram Ver1a

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