video analytics use cases in industrial iot · 2019-11-06 · video analytics use cases in...

VIDEO ANALYTICS USE CASES IN INDUSTRIAL IOT

RAMYA RAVICHANDAR

ramya@foghorn.io

2

Founded to create real-time IIoT edge intelligence platform

Million in funding from industrial leaders$47.5

Commercial IIoT engagements globally100+

Analyst report coverage in 2018, including 7 from Gartner20+

Awards for innovation, edge intelligence, ML, AI and more 25+

Partners across OEM, cloud, SI, gateway, and semiconductors 50+

‘14

Real-time edge intelligence for industrial IoT

Introducing FogHorn

Proprietary and Confidential 3

IIoT and AI Industry Recognition

1. Qualcomm2. Cisco3. Intel4. FogHorn Systems5. Amazon Web Services6. Microsoft7. Everythng8. Google9. Tesla10. IBM

10 Hot IoT Startups to Watch in 2018

Proprietary and Confidential 4

Proprietary and Confidential 5

World’s smallest and fastest

inference engine that powers

transformational solutions

FogHorn Products

6

EdgeAI Platform

Out of Box Solutions

7

STREAMING ANALYTICS AT THE EDGE

A PRIMER

Proprietary and Confidential

8

DRIVERS FOR EDGE COMPUTING

Limited Connectivity

Bandwidth Costs

Risk of Cyberattacks

Real time decision making

High fidelity analytics

Traditional Batch AnalyticsCollect→ Store→ Transmit→ Process→ Transmit→ Act

FogHorn Streaming AnalyticsStream→ Process→ Act

Proprietary and Confidential 9

Batch vs. Streaming Analytics

11001010

cloud

data center

edge device

Traditional Batch AnalyticsData at Rest

Streaming AnalyticsData in Motion

Proprietary and Confidential 10

Data at Rest vs. Data in Motion

11001010

cloud

data center

edge device

Characteristics

Historic, several minutes to hours Data Freshness→ Real-time, milliseconds

Disk based and transmitted Data Location→ In memory and local

Often downsampled Data Resolution→ High resolution and raw

Seconds to hours Processing Speed→ Milliseconds to seconds

Periodic Processing Frequency→ Continual

High Power, Large Storage Processing Power→ Lower power, only memory

Historical analysis Use Cases→ Time critical decisions

IoT Data Requires Different AnalyticsHigh Volumes, Varieties and Velocities of Data

11

Bandwidth costs of moving large volumes of data

Latency and real-timedecision making

Limited or noconnectivity

Proprietary and Confidential 12

Stream Processing Your Data with FogHorn

FogHorn’s Real-time Stream Processing Engine

Proprietary and Confidential 13

VEL® Complex Event Processing (CEP)

• Designed from the ground up in C++ for edge and small compute environments

• Performs real-time analysis of disparate asynchronous streams of sensor & control system data

• Executes complex pattern recognition and machine learning inferencing on high frequency and temporally continuous data

• Detects events, anomalies, and deviations from rules in real-time, enabling immediate control actions

• Highly advanced tooling for authoring, testing, debugging with live introspection and instrumentation of live system in production

TransportationWearables

Connected Cars

Connected Homes

Connected Cities

Industrial Internet

Manufacturing

Utilities

Oil & Gas

Healthcare

Proprietary and Confidential 14

The Rise of the Internet of Things

…IoT devices will grow to as many as 30 billion devices by 2020.McKinsey & Company. Image: Goldman Sachs.

Industries and Use Cases

15

Manufacturing, Oil & Gas, Energy, Transportation, Smart Buildings

Proprietary and Confidential

EdgeML®

Industrial IoT Data Volume Overwhelming

16

1 PB Mining

480 TB Jet engine

24 TB Automated manufacturing

1 TBLarge refinery

0.8 TB Large retail shop

0.5 TB US Smart meters

17

IOT Signals Report 2019

Continuum of IoT Use Cases

18

Edge Analytics

EdgeML®

Edge AI

19

TRANSFORMER DEFECT DETECTION

Real-time edge testing improved yields and reduced scrap

20

Transformer Defect Detection

Real-time edge testing at each stage of manufacture

for improved yields and reduced scrap

PRINT DEFECT DETECTION

Vision Model detectsvariations in printed characters and markings

21

Transformer Defect DetectionPrint Defect Detection

FINISHED GOODS INSPECTION

Deep learning algorithms identify defects through vision models

22

FACIAL RECOGNITION WITH DEEP LEARNINGDriver facial recognition as a vehicle access authentication mechanism

23

Fusing of disparate sensor data streams

to derive insights

Pressure, vibration,

temperature, etc

Audio, video, 3D imaging

Geofencing for asset tracking, perimeter

monitoring

Sensor Fusion

Proprietary and Confidential

24

VIDEO-BASED SECURITY SURVEILLANCE

Real time monitoring & identifying worker movements in restricted zones

25

HAZARD DETECTION USING VISION MODELSDetect hazardous Conditions in real time to ensure worker safety

26

FLARE MONITORINGSOLUTION

Running deep learning models on Jetson Nano

27

28

INTERESTED IN LEARNING MORE?

learning@foghorn.io