Andrea Alvarez Rodriguez e Thomas Maragno - Implementando reconhecimento facial usando Java e Oracle...

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June 28–30, 2016São Paulo

JavaOneSES12228 Implementing “Fast and FuriouFastData and Cloud Platforms God’s Eye” with Oracle’sitle

Andrea AlvarezSolution SpecialistOracle Costa Rica

Thomas MaragnoSales ConsultantOracle Caribbean


What is the “God’s Eye”?



• A pen drive?• A SD Card?



• Piece of code created by Ramsey

• “Is able to hack any type of technology.”

• Can take control of their camera

• Returns the information gathered in less than 4 min

• Can locate any given person anywhere in the world.

• It can be taken over by anyone


Demo

Oracle Confidential – Internal/Restricted/Highly Restricted 8


Demo


High Level Steps

Detected facesand crop image

Live Feed

Process facesand try to recognize

the subject

Correlate datawith static known

data

Consume correlated data from queueShow live feed

1

2a

2b3

JMS Q JMS Q


Edge Analytics


Used Components

Compute CS/DevCS JCS/DevCS

Stream Analytics

Database CS POI DB

JET AppVia JMSVia JMS

SE Embedded


Oracle Edge Analytics


“Oracle OEA (formerly known as Event Processing) is a Java serverfor the development of high-performance event drivenapplications on embedded devices. It is a lightweight Javaapplication container based on Equinox OSGi, with sharedservices, including the Oracle CEP Service Engine, which provides arich, declarative environment based on Oracle Continuous QueryLanguage (Oracle CQL).”


Adapter Channel

Processor/Query Event

1 Face DetectionConcepts


1 Face Detection

CEP Engine

Query

Query

Inp

ut

Ad

apte

rs

Ou

tpu

t A

dap

ters

event

event

event

Real-time event data Context-aware filtering, correlation, aggregation and processing of data

Processed business events for downstream applications

event

event

eventSensors

Backend Applications

High Level Architecture


1. Face Detection

Tap into the camera

Steps


1 Face Detection• Implements RunnableBean

and StreamSource Interfaces

• Convert data coming from physical devices to OEA Events

• Convert OEA Events

• Future Plans: Replace with IoTCS


Video Adapter


1. Face Detection

Send the live feed downstream.

Steps


1. Face Detection

Clean events. Discard errors and duplicates

Steps


1. Face Detection• Implements RunnableBean

and StreamSource Interfaces

• Convert data coming from physical devices to OEA Events

• Convert OEA Events



CQL / Processor


1. Face Detection

Destination: JMS Q

Steps


1. Face Detection• JMS Adapter not implemented

in Edge Analytics

• Custom JMS Adapter built

• Implements StreamSinkInterface

• Convert OEA Event to MapMessage



Custom JMS Adapter


JMS Q JMS Q


High Level Steps


Live Feed


the subject


data


1

2a

2b3


Edge Analytics


Used Components


Stream Analytics

Database CS POI DB


SE Embedded


2a. Face Recognition

Standard OOTB pre built JMS Adapter

Steps


2a and 2b. Face Recognition and Correlation

Actual face recognition occurs here

Steps


“OpenCV is one of the most popular libraries used to develop ComputerVision applications. It enables us to run many different Computer Visionalgorithms in real time. It has been around for many years, and it hasbecome the standard library in this field. One of the main advantages ofOpenCV is that it is highly optimized and available on almost all theplatforms.”

Authors of OpenCV By ExamplePrateek Joshi; David Millán Escrivá; Vinícius Godoy

What is OpenCV





Detecting objects and or Recognizing faces – implemented process

Haarcascades

Training image set


2 Face Recognition

Discard low confidence recognitions

Steps


2 Face Recognition

Send downstream (JMS) for JET App Consumption

Steps


2 Face Recognition

Testing purpose. Write event to CSV

Steps


JMS Q JMS Q


High Level Steps


Live Feed


the subject


data


1

2a

2b3


Edge Analytics


Used Components


Stream Analytics

Database CS POI DB


SE Embedded


3 Consume Messages from JET

• Collection of Open Source and Oracle-built JavaScript Libraries

• Rich Component Set with Oracle Data Visualization

• Alta Theme Built-in

• Oracle Standards Compliant

• Security.

• Common Model API.

• Unique features for mobile apps development.

• Internationalization.

JET JQuery JQueryUI Knockout RequireJS Hammer

What is JET?


3 Consume Messages from JET

• Use of Model-View-ViewModel (MVVM) architecture– Better organized code by isolating logic for managing a specific part of the UI

– Natural point for plugging in View Unit Tests

– Enables automatic View updates in response to data changes and user input, reducing the quantity of business logic (or glue) required in the code

– Enables designer-developer workflow due to loose coupling between View markup & code behind

• Use two-way data binding to ease development

38

What is JET?


Oracle JET for FaceRecon app

39

Users

RESTADF Backend

JMS Queue

Server Side

Client Browser

JET Common Model

HTTP/JSON

Google Maps Third-party Cloud API

Mobile/Web FaceReconApplication

JCS/DevCS

Business Services

Data

Model

Knockout.JS ViewModel

View

Re

qu

ireJS

JET UI Components

JQueryUI Hammer Alta UI Theme

JET rou

ting

system


Future Plans


Live Feed


the subject


data


Store videoSegments in hadoop

for analysis

JMS Q JMS Q


Unlocking Value of IoT Data via Analytics & Integration

Time

Bu

sin

ess

Val

ue

Act Now

HOTSchedule

Maintenance

WARMPlan for

next release

COLD

42


Working with Big Data: some challenges

1. Big data ≠ Infinite storageYes, storage is cheap but it helps to have clean data, with context and less redundancy

2. Hadoop is batch-oriented and there is inherent latency "With the paths that go through Hadoop [at Yahoo!], the latency is about fifteen minutes […] it will never be true real-time. " *Raymie Stata, Yahoo! CTO(June 2011)

*: http://www.theregister.co.uk/2011/06/30/yahoo_hadoop_and_realtime/

minutes

http://www.theregister.co.uk/2011/06/30/yahoo_hadoop_and_realtime/


Get ahead of the curve

1. Filter out noise (ex: data ticks with no change), add context (by correlating multiple sources), increase relevance

2. Identify certain critical conditions as you insert data in warehouse

Move time-critical analysis to front of process

Filter out, correlate

Use Event Processing techniques


Getting Ahead of the Curve

Fast Data

Big Data

minutesms

Fast Data

His

tori

cal d

epth

: dee

p

His

tori

cal d

epth

: sh

allo

w

Example: analysis of traffic patterns and congestion times for urban planning

Example: monitoring of traffic cameras to ensure given license plates are not in use on multiple vehicles Add “depth” to your fast data by merging output

of MapReduce to stream processing


Use Case: City Automation & Traffic Management

•Automatic number

plate recognition

•Red-light violation

detection

•Traffic jam detection

•Vehicle location

system

•Geo-fences

Source: http://www.flickr.com/photos/epsos/5591761716/


Use Case: Remote Patient Monitoring

•Data evaluation for

potential health

problems

•Automatic alerts to

caregivers and

healthcare providers

•Historical data analysis


Use Case: Home Automation

•Real-time pricing with

adaptive intelligence for

carbon footprint

reduction

•Automatic water usage

metering enables

conservation

• Intrusion detection


Questions?

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