Grocery Shopping Assistant
Carolina GalleguillosPixel-café / June 2 2006
Description
GroZi project (grocery shopping assistant)
• Increase independence of people with low vision (specially blind) to perform grocery shopping in a supermarket or store.
• Help to plan shopping list, walking path to the store and grocery shopping.
Motivation
• 1.3 million legally blind people in the U.S
• Grocery store are underselling to this market.
• Blind people are “high cost” customers.
• Advance research on object recognition for mobile robotics with constrained computing resources.
Motivation
Characteristics Grocery Store:• Structured Environment (+).• Controlled Lightening (+).• Maintained by staff (+).• Well indexed (+).• People moving around aisles (-).• Huge amount of products (30K) (-).
Motivation
Possible existing solutions:1. Seeing-eye dog trained.2. RFID tags (aisle, shelf,
product).3. Barcode scanning (shelf).4. Help of sighted guide/customer
service.5. Memorize store layout.6. Home delivery.
Why computer vision?
1. Limited ability of dogs.2. RFID tags bring privacy
concerns and heavy infrastructure.
3. Eye safety and mislabeling.4. Independence.5. Store layout changes
constantly.6. Autonomy.
Our Solution• Develop a handheld device that
performs visual object recognition with haptic feedback.
• Avail of complementary resources (RFID, Barcode scan, sighted guide)
We are focusing on the computer vision aspects of this problem.
MoZi Box General purpose low-cost mobile
system geared for computer vision applications. MoZi is a combination of the Mobile Vision System (MoVs) and ZigZag
• Finite memory : Compact Flash (CF) cards ranging from 256 MB to 4 GB.
• Processor speed: in the neighborhood of 60-400MHz
• Frame rate: enough snapshots to cover the shelf with some overlap (as in panoramic stitching) (15fps instead of 30fps?).
• Color Calibration: Macbeth color chart to calibrate the color space.
Use of the System
• Creating a Shopping List.• Getting to the Grocery
Store.• Navigating the Store.
Shopping List
• Online Website:– Website stores data and
images of different products.
– Feedback from users.– Provides walking path.
• Prepare shopping list.• Download information
into Mozi Box.
On the way
Separate project.• Mozi Box with GPS.• Visual waypoints.• Traffic/Street sign
reading.• Use in addition to cane
and asking sighted bystanders.
Inside the Store
• Finding aisle (OCR, RFID, ask).
• Avoiding obstacles (cane).
• Finding products (sweep of aisle, spot product, barcode check).
• Checking out (coupon and cash).
Obtaining training data
• Online Images (Web).• Collecting from MoZi box (in situ).• Collecting from embedded camera
near the barcode scanner (in situ).• Known databases (COIL-100,ETH-
80, etc.)(more research oriented)• Synthetic examples.• Active learning.
Obtaining training data
• Active learning problem:Find UPC for the corresponding image (labeling).
• Semi-supervised.• Weakly labeled.
Obtaining training data
• Sunshine Store @ UCSD.– Venue for pilot
study. – 4K items in stock. – 1749 sq. ft.
(assignable)– We want to scale
to a bigger number of products (30K).
– No bakery or vegetables.
Object Recognition
2 types of recognition (m:n, m<<n):
• Detection (of objects).• Verification (objects detected are
in that list).Algorithms: SIFT, AdaBoost cascade,
Multiclass Adaboost, Probabilistic Boosting tree, Color histogram matching, etc.
Text Detection
• Standard OCR is unlikely to be sufficient.
• Low resolution and distortion are main problems.
• Reading aisle signs, text on shelves.
Considerations
• Occlusion and clutter of products (caused by people and shopping carts).
• Multiple images of same shelf to perform “hole-fill-in”.
• Cannot fit dominant plane to the front of product shelves.
• Large number of items.
Acknowledgements
People (UCSD/Calit2):• Serge Belongie • John Miller • Stephan Steinbach • Michele Merler• Tom Duerig
Captions:Dennis Metz/ D. Stein
[X. Chen and A. Yuille ]
Questions?Comments?
