20180930Komik 2018 Agus Harjoko IR4.0 Surveilance

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A SURVEILANCE MODEL

IN INDUSTRY 4.0

Agus Harjoko

Department of Computer Science

and Electronics

Universitas Gadjah Mada,

Indonesia

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Outline

• UGM etc

• Overview

• SVS

• LPU

• DCCU

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Universitas Gadjah Mada

• Located in Yogyakarta, Indonesia

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Dept. Computer Science and

Electronics

• Study programs

• Undergraduate in Computer Science

• Undergraduate in Electronics and

Instrumentation

• Master in Computer Science

• Doctor in Computer Science

• Research labs: 5

• AI and Data Science

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Industry 1.0

–

4.0

• 1784:

mechanical

loom

• 1850: kapal

uap dll

• 1970:

electronics and

IT

• 2016:

cyber-physical

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Volume Data Growth

• International Data Corp, 2017 (1 ZB = 1 trillion giga bytes)

00.05

2

12

47

163 2005

2010

2015

2020

2025

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Key factors for data growth

• Evolution of data: business to life-critical

–

Internet activities, socmed, transportation etc

• Embedded system and IoT

–

Smart city, smart agriculture, etc

• Mobile and real-time data

–

Real-time-data-based bussiness

• AI systems change the landscape

–

AI, NLP, machine learning

• Secutiry as critical foundation

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Country readyness IR4.0

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Making Indonesia 4.0

• Roadmap Industry 4.0, April 2018

• 5 sectors

–

Food and beverage

–

Textile and apparel

–

Automotive

–

Electronics

–

Chemical

• Why?

–

60% of manufacturing GDP

–

65% of manufacturing exports

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Creating values from converging

technologies

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Surveilance in Industri 4.0

• In-door and out-door

• Traffic Monitoring

• Computer Vision Research group

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Existing traffic monitoring in

Yogyakarta, Indonesia

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Problems & Motivations

14

• Missing frames

• High computing power

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Proposed Solution

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• Distributing computing tasks

• Smart Video Sensor (SVS)

• Local Processing Unit (LPU)

• Data Center and Control Unit (DCCU)

• Need communication data format (XML)

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Illustration for proposed solution

existing

Proposed solution

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SVS Architecture

Video

Sensor

Module

Video

Compression

Module

Feature

Extraction

Module

Feature

Representation

Module

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Video Sensor Module

Video

aquisition

function

Resolution,

positioning,

lighting

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Sample frame : Galeria

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Video Compression Module

Compressing

video for

efficient

transmission

fungsi

Size, quality,

need for video

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Feature Extraction Module

Function

Pre-processing,

segmenting,

extracting

features

Parameters

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Feature Extraction Module

Frames

Segments

Video stream

Preprocessing

Segmentation

Feature

extraction

Ciri-ciri obyek

<?xml version="1.0" encoding="utf-8"?> <feature feature_extraction="program"> <headerframe target="frameNumber">90 <headerframe target="currenttime">2014-01-16T06:15:43.23+07:00

</headerframe>

<objectnumber target="Object">1</objectnumber> <featureitem target="WBB">39</featureitem> <featureitem target="HBB">46</featureitem>

<featureitem target=“ROI”>1794</featureitem>

<featureitem target="centroid">[378 42]</featureitem> <featureitem target="aspectratio">1</featureitem> <featureitem target="PBB">170</featureitem>

:

<featureitem target="Dispersedness">14.7226427583197</featureitem>

:

<featureitem target=“ROI">2380</featureitem>

:

<featureitem target="Dispersedness">16.7321836705882</featureitem> </headerframe>

</feature>

</headerframe>

:

</feature>

</headerframe>

:

</feature>

</headerframe>

:

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Pre-processing example

23 frame

Histogram

Equalization

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Frame before and after segmentation

24 frame

Gaussian

Mixture

Model

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Morphological Image Processing

25 morphological

opening and

closing

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Feature Representation Module

Function

Representing

features

Parameters

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Representation scheme

27 Object : 1

F1: value

F2: value

Fn: value

LocationNm: value VideoNm: value FrameNo: value Time: YYYY-MM-DDThh:mm:ss.sTZD

Object : 2

F1: value

F2: value

Fn: value

Object : n

F1: value

F2: value

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XML Structure

<?xml version="1.0" encoding="utf-8"?>

<Z feature_XML="list">

<HFrame Value ="LocationNm"> galeria </HFrame>

<HFrame Value ="VideoNm"> galeria_4 </HFrame>

<HFrame Value ="FrameNo">194

<HFrame Value ="FTime">2014-01-16T06:10:15.21+07:00

</HFrame>

<ObjectNo Value ="Object"> 1 </ObjectNo>

<Feature Value ="WBB">156</Feature>

<Feature Value ="HBB">149</Feature>

<Feature Value ="ROI">23244</Feature>

<Feature Value ="centroid">[368 383] </Feature>

<Feature Value ="aspectratio">1</Feature>

<Feature Value ="PBB">610</Feature>

:

<Feature Value ="Dispersedness">15.154932</Feature>

:

<ObjectNo Value ="Object"> 3 </ObjectNo>

<Feature Value ="WBB">22</Feature>

<Feature Value ="HBB">37</Feature>

<Feature Value ="ROI">814</Feature>

<Feature Value ="centroid">[166 156] </Feature>

<Feature Value ="aspectratio">1</Feature>

<Feature Value ="PBB">118</Feature>

:

<Feature Value ="Dispersedness">15.233610</Feature>

</HFrame>

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Transmission Module

Function

Transmitting

compressed

video and

features in XML

format

Parameters

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Local Processing Unit

• Working for local needs

• Receives features from the SVS

• converts features from XML to numbers,

• selects the appropriate features based on

LPU’s

special needs

• processes the selected features

• results may be communicated with the DCCU

• Example:

• timing local traffic light controller,

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Example: XML reading

</headerframe>

<featureitem target= ROI > </featureitem>

:

<featureitem target= ROI"> </featureitem>

:

</feature>

</headerframe>

:

</feature>

</headerframe>

:

</feature>

</headerframe>

:

</feature>

</headerframe>

:

</feature>

</headerframe>

:

</feature>

XML representation

Convert

XML



numbers

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Data Center and Control Unit

• Coordinating LPUs in a certain region

• Stores compressed video from SVS

• May sends signals to LPUs

• Receives object features extracted from SVS

• Process features for regional needs

• Example:

• synchronizing traffic light controllers,

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Applications

• Traffic object classification

• Tracking of classified object

• Licence plate recognition

• Licence plate and brand name recognition

• Traffic light controller timing

• Illegally parked vehicle identification

• Warehouse monitoring

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Classification of traffic objects

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Thank you

Terima kasih

Matur nuwun

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