This talk focused on emerging data, analytic and technology capabilities shaping 'retail stores of the future' shared at Data Works Summit in 2019.

1. © Cloudera, Inc. All rights reserved.
COMPUTER VISION:
COMING TO A STORE
NEAR YOU
Brent Biddulph, Managing Director, Retail & CG, Cloudera
Florian Muellerklein, Data Scientist, Miner & Kasch

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AGENDA
Industry Trends
Business Drivers
Use Cases
How it Works
Bringing It All Together

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86% …OF RETAIL SALES
WILL STILL OCCUR IN-
STORE IN 2021.
(STATISTA, 2018)

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Mobile
Apps
ESLs,
Robots
Autonomous
Vehicles
Magic
Mirrors+
Shelf, Bin,
& Rack
Sensors
Surveillance
CamerasCompetitive
PricingSatellite
Images
Logistics &
Asset
TelematicsIn Home
Devices Frictionless
Checkout
RFID
Streams
SINGLE VIEW
OF CUSTOMER
FRICTIONLESS
COMMERCE
UBIQUITOUS
FULFILLMENT
RELEVANT
INTERACTIONS
OPERATIONAL
EFFICIENCY
Streaming Capabilities Enable New Insights,Right-Time Response
New Data Sources Extend Retail Intelligence = video, image data

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BUSINESS IMPROVEMENT
OPPORTUNITIES FOR
COMPUTER VISION IN-STORE
• Enabling Frictionless Commerce
• Improving Operational Efficiencies
• Improving Customer Experiences
• Reducing Fraud & Shrink
$410B
“AI / IOT ECONOMIC VALUE-
ADD IN RETAIL BY 2025,
WITH SOME OF THE MOST
VALUABLE USE CASES
DRIVEN BY STREAMING
VIDEO ANALYTICS.”
(MCKINSEY, 2017)
REAL-TIME PROCESSING –IMPROVED ACCURACY – DEEPER INSIGHTS

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ENABLING
FRICTIONLESS
COMMERCE
Amazon Go Store
• Computer Vision (100s
of cameras on shelves, ceiling)
• Sensor Fusion (cameras,
shelf scales)
• Deep Learning (advanced
pattern recognition, 100s of
algorithm’s)

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ENABLING
FRICTIONLESS
COMMERCE
Alibaba’s HEMA store
• Robots, apps, facial
recognition and
overhead conveyor
belts for fulfillment
delivery
• Using facial recognition
customers can pay
using their face - no QR
code nor credit card
swipe required

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IMPROVING
OPERATIONAL
EFFICIENCIES
Out-of-Stock &
Merchandising
Execution
• Out-of-Stock
Notifications
• Price and Promotion
Compliance
• Schematic & Display
Compliance
Photo Source: Retail Technology Corp.

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24% …OF AMAZON REVENUE
CAN BE ATTRIBUTED TO
BRICK & MORTAR STORE
OUT-OF-STOCKS.
(IHL, 2018)
OPPORTUNITY: IMPROVED OUT OF STOCK RESPONSE

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IMPROVING
OPERATIONAL
EFFICIENCIES
Advertising &
Promotional
Execution:
• Day-Part Promotional
Execution
• Audience and
Conversion Insights
• Proximity Marketing

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$174B
IN CPG ANNUAL TRADE PROMO SPEND.
OF THAT, 17% ($300M) SPENT ON SHELF,
YET 68% OF CONSUMER DECISIONS ARE
MADE AT THE SHELF.
IRI, 2016
OPPORTUNITY: IMPROVED MERCHANDISING EXECUTION INSIGHTS

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IMPROVING
CUSTOMER
EXPERIENCES
• Identify VIPs, ensure
high-touch CX
(‘clienteling’)
• Understand, respond
(anonymously) with
proactive customer
service opportunities

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• Simplifying the
dressing room
experience
• Assist to complete
the ‘look’ via cross-
sell of matching
shoes, accessories
• Reduce likelihood of
returns
IMPROVING
CUSTOMER
EXPERIENCES

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$260B
IN MERCHANDISE IS RETURNED TO
RETAILERS EACH YEAR.
AS MUCH AS 40% OF ONLINE
CLOTHING PURCHASES ARE RETURNED.
NRF, 2016
OPPORTUNITY: IMPROVED CX + MERCHANDISE RETURNS

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The use cases for CV
are promising…driving
meaningful business
impact, even creating
new revenue models
Frictionless Checkout
Loss Prevention
Neighborhood Insights Personalization
Customer Insights Customer Engagement
Dynamic Merchandising
Autonomous DeliverySource: Allure Source: Ahold/Delhaize
Source: Caper
Source: Orbital Insight
Source: Aura visionSource: Trigo-vision

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HOW IT WORKS AND HOW YOU CAN DO IT

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HOW DOES IMAGE RECOGNITION WORK?
Detect & Score
Convolutional neural
network analyzes
images and produces
actionable
representations
Compare
Representation manifolds can be mapped to find
matches, similar, or complementary images
Match & Alert
Users alerted and
shown matches

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IMPLEMENTING THESE TECHNIQUES IN YOUR COMPANY
• Options for implementing these functionalities in your store
• Purchase a point service for the desired functionality
• Whether it’s the whole solution or just part of the solution
• Hire a company to design and implement a custom solution
• Shameless plug for my company
• Implement the solutions yourself!

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IMPLEMENTING THESE TECHNIQUES IN YOUR COMPANY
• Need to define the problem and break it down into smaller parts
• There are a few CV modalities where machine learning shines
• Think of a modality as a smaller subproblem in machine perception
• May have multiple modalities within one problem
• A few different models may need to run
• Design supporting infrastructure for ML models
• Method to feed data into model(s)
• Methods to interpret model output(s)
• System to chain together multiple models
• Translate from model output to human interpretable results

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MACHINE LEARNING AND COMPUTER VISION

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DEEP LEARNING
• Many computer vision applications are now
becoming successful due to advances in
deep learning
• A deep learning model for computer vision is
specifically designed to exploit the spatial
dependencies in image data
• Flexible architecture designs to produce
desired output for given problem domain

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COMPUTER VISION PROBLEMS
• Computer vision problems can often be broken down into several problem
types
• Can make global inferences about an image
• Multiple spatially aware inferences
• Pixel level inference for very fine grained output needs
• Learning latent representations

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GLOBAL INFERENCE
• We may want a model to produce only a single output for a given image
• Identify the subject of the image (type of inventory, type of object)
• Identify affect of a person’s face
• Qualitative assessment of scene or object (does shelf need organization, is inventory
damaged)
• Infer distance between two objects
Gender
Affect
Eyeglasses
Headwear

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SPATIALLY AWARE INFERENCE
• We may want a model to produce many outputs for a given image
• Describes the locations and classifications for objects in an image
• Locate customers in your stores (counts, locations, heatmaps, time lingering, …)
• Locate products on a shelf
Aura Vision, https://auravision.ai

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PIXEL LEVEL INFERENCE
• We may want to produce an output for every
pixel in an image
• Classify each pixel as what object it’s a part of
• Produce mapping or overlay for the entire image
• Probability map of object location
• Depth map of a room

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LATENT REPRESENTATION LEARNING
• Finally, we may want generate
some kind of representation of
an image that allows us to
perform some useful math
• Representations capture semantic
information
• Quantify similarity metrics
between two images
• Train a model on a closely related
task that forces it to learn good
representations

27. © Cloudera, Inc. All rights reserved. 27© Cloudera, Inc. All rights reserved.
EXAMPLE USE CASE

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EXAMPLE USE CASE
• Some combination of these tools and pre/post model logic can be used to
build a real retain computer vision use-case
• Lets assume that we want to build a tool to help our customers by providing
clothing recommendations with CV
• They come into our store looking for a specific style of clothing
• Arrive with image of desired style
• Set up kiosk for clothing recommendations
• Augment magic mirror

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FASHION PRODUCT RECOMMENDATION EXAMPLE
• In order to create this application we’ll need two different machine learning
models and some logic around the model outputs
• Some capability to interpret the image and find the targeted piece of clothing
• Using a segmentation model
• Extract targeted article of clothing from image
• Create representation of clothing that allows it to be compared to others (model b)
• Create representations of entire catalog
• Matching algorithm to find closest match to query article

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Segmentoutthevariousarticlesofclothing

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Createsemanticrepresentationsforourentirecatalog

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Extractthetypeofclothingthatwewanttomatchon

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Createsemanticrepresentationsfromthoseextracted
Numeric vector
representing
visual patterns
and attributes

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Comparethoserepresentationstothoseofourcatalog
A
B
Best Match

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Puttingittogether

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DEMO

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BRINGING IT ALL TOGETHER

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WHATINDUSTRIALIZED“EDGETOAI”LOOKSLIKE
Streaming
Ingest
Batch Ingest
Machine
Learning Tools
BI Tools and
SQL Editors
Data Products
DATA, METADATA, SECURITY, GOVERNANCE, WORKLOAD
MANAGEMENT
MACHINE
LEARNING
DATA
ENGINEERING
DATA
WAREHOUSE
OPERATIONAL
DATABASE

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THANK YOU
Let’s Keep the Conversation Going…
Brent Biddulph
MD, Retail & CG, Cloudera
bbiddulph@cloudera.com
+1 425.273.6851
www.linkedin.com/in/brentbiddulph/
@brentbiddulph
Florian Muellerklein
Data Scientist, Miner & Kasch
fmuellerklein@minerkasch.com
+1 410.564.1720
www.linkedin.com/in/florian-muellerklein/
@mllrkln