SlideShare a Scribd company logo
Disruptive trends fueled
by AI and camera
edge analytics
Memoori Webinar
Nikolas Fröhle, Product Growth Manager
November 2020
Security and Safety Things is
a fully owned but independent
Bosch start-up headquartered
in Munich, Germany. We were
founded in 2018.
www.securityandsafetythings.com
Security & Safety Things
@SecuritynSafety
Product Growth Manager
Nikolas Fröhle
Hello
10/29/2020Disruptive trends fueled by AI and camera edge analytics 2
Nikolas.Froehle@de.bosch.com
Our approach and mission
Our platform
COMPANY & PLATFORM01
AI video analytics
Edge computing
Open marketplaces
DISRUPTIVE TECHNOLOGY02
Security and safety
Retail analytics
Build your own sensor
SMART BUILDING USE CASES03
Automated traffic management
Pattern recognition
Autonomous driving with data economy
SMART CITY USE CASES04
Agenda
10/29/2020Disruptive trends fueled by AI and camera edge analytics 3
Company & Platform
Our approach, platform and
partners
01
10/29/2020Disruptive trends fueled by AI and camera edge analytics
A Bosch start-up
100% daughter company of
Robert Bosch Group, run fully
independently
110+ employees globally
Our team has scaled worldwide
across 3 locations: HQ Munich,
Eindhoven and Pittsburgh
Member of OSSA
We are a strategic partner of the
industry alliance with a shared
objective of driving innovation
through standardization
Who is Security & Safety Things?
About us
Disruptive trends fueled by AI and camera edge analytics 510/29/2020
We turn security cameras
into multi-purpose IoT
sensors.
With one standard OS that
runs across camera
brands and an Application
Store with a vast selection
of AI-enabled apps.
10/29/2020Disruptive trends fueled by AI and camera edge analytics 6
S&ST is supported by the leading
industry Alliance OSSA
Platform & Partners
Disruptive trends fueled by AI and camera edge analytics
Application Store
Development
Environment
Camera OS &
Infrastructure
Device Portal
An open platform and marketplace
AI applications run on a state-of-the-art OS, driving advance safety
and security and new business insights in an open ecosystem.
Innovation with standardization
Provide framework for driving standardization while allowing for
differentiation where beneficial
710/29/2020
Founding
members Further members
ONVIF specifications and OSSA standards are
complementing rather than competing
OSSA vs. ONVIF
10/29/2020Disruptive trends fueled by AI and camera edge analytics 8
• Industry forum to ensure interoperability in
transition from analogue to IP cameras
• Defines communication standards
→ communicate together
• Members mainly from the camera
manufacturer business
• Alliance founded to create an open
ecosystem that allows for new innovative
solutions
• Create standard components
→ build together
• OSSA members represent all major
stakeholders in the ecosystem to jointly
shape specifications
• ONVIF interfaces are used in OSSA’s
communication components to ensure a
consistent experience
The place for AI and analytics camera apps
Application Store
10/29/2020Disruptive trends fueled by AI and camera edge analytics 9
The fastest growing Application Store for smart
cameras. See for yourself at
store.securityandsafetythings.com
‣ 75+ apps available
‣ From 30+ developer partners
‣ 30+ use cases
‣ 25+ industries
Use cases Industries
Disruptive tech trends
Deep learning & edge
computing
02
10/29/2020Disruptive trends fueled by AI and camera edge analytics
Artificial Intelligence Computer Vision
Many buzz words - what is it, actually?
AI, Computer Vision, Deep Learning, Analytics…
10/29/2020Disruptive trends fueled by AI and camera edge analytics 11
Machine Learning is based on computers that automatically learn how to
perform a given task without being explicitly programmed with rules.
Artificial Intelligence is the idea of giving computers the ability to think as
intelligently as humans, learning from data.
Focus: Computer Vision, that uses algorithms to
understand images and videos, the same way a human eye does.
Neural Networks are based on artificial neurons that form a network. Each
neuron is a mathematical function that takes a weighted combination of
inputs and produces an output.
Deep Learning is based on “deep” neural networks with three or more
hidden layers. This became relevant with GPU computing power.
Machine Learning
Neural Networks
Deep Learning
Computing power and new algorithms are changing video
analytics outcomes rapidly
AI video analytics
10/29/2020Disruptive trends fueled by AI and camera edge analytics 12
• High calibration effort
• Many false positives
• Inflexible system (hard-coded)
• Little to none calibration
• Inferencing on the edge (soft-coded)
• Pre-trained open source models are available
allowing for simple adoption for use cases
Rule-based Video Analytics AI-based Video Analytics
Up to now:
Analytics not very
reliable, still manual
interference
required.
Future:
Less human
intervention, only
relevant footage is
required. Less
storage needs.
Hard-coding example
Specific parameters are used to
predict whether an animal is a cat,
e.g. if an animal’s length and color
lies within a certain range, it is a
cat.
Soft-coding example
A data set containing animals
labelled with their species type
and characteristics are fed to a
computer. A computer program
predicts whether an animal is a
cat or not based on the data set.
Cloud
Benefits
Powerful SoCs enable faster, cheaper, more
reliable and GDPR-compliant data
processing.
Access to uncompressed video pipeline allows for better
analytics results
Low network bandwidth required, no latency issues
Less network infrastructure required results in lower solution
costs
Allows for data privacy (GDPR compliance)
Decentralized analytics at the
data source now becomes
efficient
Edge computing
10/29/2020Disruptive trends fueled by AI and camera edge analytics 13
On-premise
Edge
Currently, data processing for
connected devices happens mostly in
the cloud.
Sending data back and forth across
the network can take too long — and
requires expensive infrastructure.
Cameras with powerful CPUs and
GPUs enable data processing
already at the source — and thus
allow for local decisions.
Edge
Emergence of marketplaces for 3rd party developers enable
meaningful applications on each part of the network
Open marketplaces
10/29/2020Disruptive trends fueled by AI and camera edge analytics 14
De-centralized
architecture
• Less hardware,
more software focus
Increased flexibility
• Low integration and
calibration effort –
fast & cost-effective
updates & re-
purposing possible
Sales channel shift
• Software purchase
shifts from pre-
install to post-install
Cloud
On-premise
AWS
Mobotix
Microsoft Azure
Milestone
Panasonic
Axis
S&ST
Fast & flexible installation.
Low calibration effort
Pre-trained AI models are
available.
Ubiquitous data
Less hardware
infrastructure.
Lower costs
Open marketplaces allow
for infinite app use cases.
Large selection of apps
Self-improving AI models
through retraining.
Better app results
Higher solution acceptance.
More data privacy
AI on the edge is a
powerful architecture
for video systems.
This leads to many interesting new use
cases in the future.
Summary
10/29/2020Disruptive trends fueled by AI and camera edge analytics 15
Smart building
Use cases for the future
03
10/29/2020Disruptive trends fueled by AI and camera edge analytics
Classic use cases are solved in more efficient ways –
IoT platforms become video centric.
Security & Safety
10/29/2020Disruptive trends fueled by AI and camera edge analytics 17
Fire detection
A highly regulated market, fire detectors will stay
relevant in most use cases. Yet, in some areas
cameras serve as additional, fast detector.
Access Control
Moving away from access cards to face recognition
software.
Video analytics can help offline retailers become as data
driven as their e-commerce competitors
Retail Analytics
10/29/2020Disruptive trends fueled by AI and camera edge analytics 18
Demographics
Understanding
customers - who is
interested in what?
Dwell time
How long do
prospects look at
certain products?
Self-checkout
Customers check out
fast & easy, store
stays in control.
Customer
Satisfaction
Insights on shopping
experience.
Real-time funnel &
conversion
analysis
Optimize and track
results.
Training suites allow training your own sensor based on
pre-recorded video footage – solving individual use cases
Build your own sensor
10/29/2020Disruptive trends fueled by AI and camera edge analytics 19
Efficiency gains
Daily tasks can be solved with
one camera that can cover
several use cases at once.
Examples
• Building occupancy
• Parcel delivery detection
• Location of specific objects
123
Feed dataTrainDeploy &
Run
Smart city
Use cases for the future
04
10/29/2020Disruptive trends fueled by AI and camera edge analytics
Computers react immediately based on real-time data and
predictions
Traffic management
10/29/2020Disruptive trends fueled by AI and camera edge analytics 21
Cars &
passengers
People masses
at events
Real-time analytics &
decision making
Video control rooms will
disappear over time as apps
can more accurately observe
cameras.
In case of events, computers
can make decisions based on
predictions.
• Traffic jams
• Accidents
• Number of passengers in car
• Social distancing
• Mask detection
• Blocked exits, overcrowded walkways
Based on collected data, AI powered apps recognize
traffic patterns to make traffic safer and more efficient.
Improve road safety and traffic
flow
▪ Detect accident critical roadways and crossings
(e.g. count nearby misses)
▪ Understand and eliminate traffic jam causes
Pattern recognition in cities
10/29/2020Disruptive trends fueled by AI and camera edge analytics 22
Real-time video data and insights from stationary
and built-in vehicle cameras are constantly shared
among traffic participants.
Shared sensor data lead to
better insights for all
▪ Reduction in accidents
▪ Vehicles warned about upcoming accidents, congestions,
etc.
▪ Improved traffic flow
▪ Adjustable speed limits & traffic light switching based on
traffic
Data economy
10/29/2020Disruptive trends fueled by AI and camera edge analytics 23
The place for AI and analytics camera apps
Application Store
10/29/2020Disruptive trends fueled by AI and camera edge analytics 24
The fastest growing Application Store for smart
cameras. See for yourself at
store.securityandsafetythings.com
‣ 75+ apps available
‣ From 30+ developer partners
‣ 30+ use cases
‣ 25+ industries
Thank you for
your attention
Product Growth Manager
Nikolas Fröhle
Nikolas.Froehle@de.bosch.com

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Disruptive Trends Fueled by AI & Camera Edge Analytics

  • 1. Disruptive trends fueled by AI and camera edge analytics Memoori Webinar Nikolas Fröhle, Product Growth Manager November 2020
  • 2. Security and Safety Things is a fully owned but independent Bosch start-up headquartered in Munich, Germany. We were founded in 2018. www.securityandsafetythings.com Security & Safety Things @SecuritynSafety Product Growth Manager Nikolas Fröhle Hello 10/29/2020Disruptive trends fueled by AI and camera edge analytics 2 Nikolas.Froehle@de.bosch.com
  • 3. Our approach and mission Our platform COMPANY & PLATFORM01 AI video analytics Edge computing Open marketplaces DISRUPTIVE TECHNOLOGY02 Security and safety Retail analytics Build your own sensor SMART BUILDING USE CASES03 Automated traffic management Pattern recognition Autonomous driving with data economy SMART CITY USE CASES04 Agenda 10/29/2020Disruptive trends fueled by AI and camera edge analytics 3
  • 4. Company & Platform Our approach, platform and partners 01 10/29/2020Disruptive trends fueled by AI and camera edge analytics
  • 5. A Bosch start-up 100% daughter company of Robert Bosch Group, run fully independently 110+ employees globally Our team has scaled worldwide across 3 locations: HQ Munich, Eindhoven and Pittsburgh Member of OSSA We are a strategic partner of the industry alliance with a shared objective of driving innovation through standardization Who is Security & Safety Things? About us Disruptive trends fueled by AI and camera edge analytics 510/29/2020
  • 6. We turn security cameras into multi-purpose IoT sensors. With one standard OS that runs across camera brands and an Application Store with a vast selection of AI-enabled apps. 10/29/2020Disruptive trends fueled by AI and camera edge analytics 6
  • 7. S&ST is supported by the leading industry Alliance OSSA Platform & Partners Disruptive trends fueled by AI and camera edge analytics Application Store Development Environment Camera OS & Infrastructure Device Portal An open platform and marketplace AI applications run on a state-of-the-art OS, driving advance safety and security and new business insights in an open ecosystem. Innovation with standardization Provide framework for driving standardization while allowing for differentiation where beneficial 710/29/2020 Founding members Further members
  • 8. ONVIF specifications and OSSA standards are complementing rather than competing OSSA vs. ONVIF 10/29/2020Disruptive trends fueled by AI and camera edge analytics 8 • Industry forum to ensure interoperability in transition from analogue to IP cameras • Defines communication standards → communicate together • Members mainly from the camera manufacturer business • Alliance founded to create an open ecosystem that allows for new innovative solutions • Create standard components → build together • OSSA members represent all major stakeholders in the ecosystem to jointly shape specifications • ONVIF interfaces are used in OSSA’s communication components to ensure a consistent experience
  • 9. The place for AI and analytics camera apps Application Store 10/29/2020Disruptive trends fueled by AI and camera edge analytics 9 The fastest growing Application Store for smart cameras. See for yourself at store.securityandsafetythings.com ‣ 75+ apps available ‣ From 30+ developer partners ‣ 30+ use cases ‣ 25+ industries Use cases Industries
  • 10. Disruptive tech trends Deep learning & edge computing 02 10/29/2020Disruptive trends fueled by AI and camera edge analytics
  • 11. Artificial Intelligence Computer Vision Many buzz words - what is it, actually? AI, Computer Vision, Deep Learning, Analytics… 10/29/2020Disruptive trends fueled by AI and camera edge analytics 11 Machine Learning is based on computers that automatically learn how to perform a given task without being explicitly programmed with rules. Artificial Intelligence is the idea of giving computers the ability to think as intelligently as humans, learning from data. Focus: Computer Vision, that uses algorithms to understand images and videos, the same way a human eye does. Neural Networks are based on artificial neurons that form a network. Each neuron is a mathematical function that takes a weighted combination of inputs and produces an output. Deep Learning is based on “deep” neural networks with three or more hidden layers. This became relevant with GPU computing power. Machine Learning Neural Networks Deep Learning
  • 12. Computing power and new algorithms are changing video analytics outcomes rapidly AI video analytics 10/29/2020Disruptive trends fueled by AI and camera edge analytics 12 • High calibration effort • Many false positives • Inflexible system (hard-coded) • Little to none calibration • Inferencing on the edge (soft-coded) • Pre-trained open source models are available allowing for simple adoption for use cases Rule-based Video Analytics AI-based Video Analytics Up to now: Analytics not very reliable, still manual interference required. Future: Less human intervention, only relevant footage is required. Less storage needs. Hard-coding example Specific parameters are used to predict whether an animal is a cat, e.g. if an animal’s length and color lies within a certain range, it is a cat. Soft-coding example A data set containing animals labelled with their species type and characteristics are fed to a computer. A computer program predicts whether an animal is a cat or not based on the data set.
  • 13. Cloud Benefits Powerful SoCs enable faster, cheaper, more reliable and GDPR-compliant data processing. Access to uncompressed video pipeline allows for better analytics results Low network bandwidth required, no latency issues Less network infrastructure required results in lower solution costs Allows for data privacy (GDPR compliance) Decentralized analytics at the data source now becomes efficient Edge computing 10/29/2020Disruptive trends fueled by AI and camera edge analytics 13 On-premise Edge Currently, data processing for connected devices happens mostly in the cloud. Sending data back and forth across the network can take too long — and requires expensive infrastructure. Cameras with powerful CPUs and GPUs enable data processing already at the source — and thus allow for local decisions.
  • 14. Edge Emergence of marketplaces for 3rd party developers enable meaningful applications on each part of the network Open marketplaces 10/29/2020Disruptive trends fueled by AI and camera edge analytics 14 De-centralized architecture • Less hardware, more software focus Increased flexibility • Low integration and calibration effort – fast & cost-effective updates & re- purposing possible Sales channel shift • Software purchase shifts from pre- install to post-install Cloud On-premise AWS Mobotix Microsoft Azure Milestone Panasonic Axis S&ST
  • 15. Fast & flexible installation. Low calibration effort Pre-trained AI models are available. Ubiquitous data Less hardware infrastructure. Lower costs Open marketplaces allow for infinite app use cases. Large selection of apps Self-improving AI models through retraining. Better app results Higher solution acceptance. More data privacy AI on the edge is a powerful architecture for video systems. This leads to many interesting new use cases in the future. Summary 10/29/2020Disruptive trends fueled by AI and camera edge analytics 15
  • 16. Smart building Use cases for the future 03 10/29/2020Disruptive trends fueled by AI and camera edge analytics
  • 17. Classic use cases are solved in more efficient ways – IoT platforms become video centric. Security & Safety 10/29/2020Disruptive trends fueled by AI and camera edge analytics 17 Fire detection A highly regulated market, fire detectors will stay relevant in most use cases. Yet, in some areas cameras serve as additional, fast detector. Access Control Moving away from access cards to face recognition software.
  • 18. Video analytics can help offline retailers become as data driven as their e-commerce competitors Retail Analytics 10/29/2020Disruptive trends fueled by AI and camera edge analytics 18 Demographics Understanding customers - who is interested in what? Dwell time How long do prospects look at certain products? Self-checkout Customers check out fast & easy, store stays in control. Customer Satisfaction Insights on shopping experience. Real-time funnel & conversion analysis Optimize and track results.
  • 19. Training suites allow training your own sensor based on pre-recorded video footage – solving individual use cases Build your own sensor 10/29/2020Disruptive trends fueled by AI and camera edge analytics 19 Efficiency gains Daily tasks can be solved with one camera that can cover several use cases at once. Examples • Building occupancy • Parcel delivery detection • Location of specific objects 123 Feed dataTrainDeploy & Run
  • 20. Smart city Use cases for the future 04 10/29/2020Disruptive trends fueled by AI and camera edge analytics
  • 21. Computers react immediately based on real-time data and predictions Traffic management 10/29/2020Disruptive trends fueled by AI and camera edge analytics 21 Cars & passengers People masses at events Real-time analytics & decision making Video control rooms will disappear over time as apps can more accurately observe cameras. In case of events, computers can make decisions based on predictions. • Traffic jams • Accidents • Number of passengers in car • Social distancing • Mask detection • Blocked exits, overcrowded walkways
  • 22. Based on collected data, AI powered apps recognize traffic patterns to make traffic safer and more efficient. Improve road safety and traffic flow ▪ Detect accident critical roadways and crossings (e.g. count nearby misses) ▪ Understand and eliminate traffic jam causes Pattern recognition in cities 10/29/2020Disruptive trends fueled by AI and camera edge analytics 22
  • 23. Real-time video data and insights from stationary and built-in vehicle cameras are constantly shared among traffic participants. Shared sensor data lead to better insights for all ▪ Reduction in accidents ▪ Vehicles warned about upcoming accidents, congestions, etc. ▪ Improved traffic flow ▪ Adjustable speed limits & traffic light switching based on traffic Data economy 10/29/2020Disruptive trends fueled by AI and camera edge analytics 23
  • 24. The place for AI and analytics camera apps Application Store 10/29/2020Disruptive trends fueled by AI and camera edge analytics 24 The fastest growing Application Store for smart cameras. See for yourself at store.securityandsafetythings.com ‣ 75+ apps available ‣ From 30+ developer partners ‣ 30+ use cases ‣ 25+ industries
  • 25. Thank you for your attention Product Growth Manager Nikolas Fröhle Nikolas.Froehle@de.bosch.com