Carolina Galleguillos and Serge BelongieCarolina Galleguillos and Serge Belongie

Department of Computer Science and Engineering, UCSDDepartment of Computer Science and Engineering, UCSD

{cgallegu,sjb}@cs.ucsd.edu

Grocery shopping is a common activity that people all over the world perform on a regular basis. Unfortunately, grocery stores and supermarkets are still largely inaccessible to people with visual impairments, as they are generally viewed as "high cost" customers.

We propose to develop a computer vision based grocery shopping assistant based on a handheld device with haptic feedback that can detect different products inside of a store, thereby increasing the autonomy of blind (or low vision) people to perform grocery shopping.

Our solution makes use of new computer vision techniques for the task of visual recognition of specific products inside of a store as specified in advance on a shopping list. These techniques can avail of complementary resources such as RFID, barcode scanning, and sighted guides.

We also present a challenging new dataset of images consisting of different categories of grocery products that can be use for object recognition studies.

General purpose low-cost mobile system geared for computer vision applications.

Abstract

GroZi: a Grocery Shopping GroZi: a Grocery Shopping Assistant for the BlindAssistant for the Blind

Use of the System

MoZi Box

• 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) .

• Color Calibration: Macbeth color chart to calibrate the color space.

Motivations

• Increase independence of people with low vision (specially blind) to perform grocery shopping in a supermarket or store.

• There are 1.3 million legally blind people in the U.S.

• Help to plan shopping list, walking path to the store and grocery shopping.

• Advance research on object recognition for mobile robotics with constrained computing resources.

Dataset

Online Website:• Website stores data and images of different products.• Feedback from users.• Provides walking path.

Prepare shopping list:• Download information into Mozi Box.

Create a Shopping List

Get to the Grocery Store

• Separate project.• Mozi Box with GPS.• Visual waypoints.• Traffic/Street sign reading.• Use in addition to cane and asking sighted bystanders.

Navigate 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 Data

Training data was obtained from two major sources:

• Sunshine Store @ UCSD (video capture) - ~30 minutes of capture - Divided into 29 .avi files - Boxes containing product manually cropped

• Web (online images): - Froogle, Shopwiki, Amazon.

- Groceries, Yahoo images - General + specialized (UPC code) queries.

Test data will be obtained from: • Collected videos/images from MoZi box (in situ).• Collected videos/images from embedded camera near the barcode scanner (in situ).• Known databases (COIL-100,ETH-80, etc.) (in vitro)• Synthetic examples (in vitro).

Future Directions

Object Recognition

Two types of recognition (m:n, m<<n): • Detection (of objects).• Verification (objects detected are in that list).

Algorithms:• Color histogram matching. • SIFT features matching.• Haar like features in Adaboost framework.• Shared features.

Integral histogram applied for better performance [Porikli, 2005].

Processing time: 25 frames per second.

CbCr chrominance plane. L2 distance between histograms is used for matching.

CbCr Histograms

SIFT keypoints

Haar features

Webimage

captured image

• Obtain a final version of the data set that can be viewed online, which is continuously updated, with more products and user interaction.

• Test detection and recognition of products using SIFT on top of Haar like features.

• Apply color Haar like features (opponent channels) for a better detection.

• Bar code scanning to perform active learning in order to learn relations between barcodes and product images.

The GroZi data set corresponds to 128 different grocery products, that have been obtained from online images and collected videos.

Special thanks to Stephan Steinbach from Calit2, Michele Merler and Tom Duerig from UCSD computer science department.

Acknowledgments

Text detection is also part of the GroZi project.

Adaboost and Haar features are used to detect text on images.