"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;
Showing posts with label SmartRetail. Show all posts
Showing posts with label SmartRetail. Show all posts

December 19, 2022

SmartRetail - GCP Architecture

 


Have been experimenting/pitching with little or no success. Either people see it as a commodity or see it as an extensive hardware investment. The willingness to experiment with key sections / innovate is hardly there.
  • People counting
  • Smart Shelf
  • Restocking
  • Heatmaps
  • Dynamic Ads
  • Sweethearing
Some key slides from Talk


  • Vision for item placement
  • Vision for item counting
  • Vision for Ads placement







GCP Architecture


  • Models run on GCP Kubernetes Engine
  • API Gateway
  • Images stored in Blobs
  • Results in Postgres / BigQuery


  • Apps for Endusers
  • Notifications
I have pitched and failed but happy to see Infliect, Tangoeye, and other companies shining in this space.

Keep Exploring!!!

June 23, 2022

Retail & Vision - 2.0 Learning

Interesting paper and discussions link

Session #1 - AI for Retail Problems

  • AI in retail

  • Same product with variations

  • Leverage a combination of techniques



  • Approach to learn


Session - #2 - Vision for Fashion

  • Customizing existing designs to make it better

  • Fashion++ alter the design

  • Training based on 
  • Classifier for fashion estimation
  • Suggesting changes


Paper - C O M P U T E R V I S I O N A N D D E E P L E A R N I N G F O R R E TA I L S T O R E

M A N A G E M E N T

  • Automatic recognition of items on store shelves and obtaining a reliable 3D reconstruction of an environment for navigation purpose
  • Planogram - This arrangement is carefully planned to maximize sales and keep customers happy, currently, however, verifying compliance of real shelves


  • Planogram as a grid-like fully connected graph
  • Planogram compliance issue related to the checked node is reported (i.e., missing/misplaced item).
  • Camera would locally run only model relevant for the Aisle
  • Given a shelf image, we perform first a class-agnostic object detection to extract region proposals enclosing the individual product items

  • we propose to deploy an image-to-image translation GAN together with the embedding CNN and to optimize the whole architecture end-to-end

More Reads

Keep Exploring!!!