We worked with a leading provider of digital media player solutions in the digital signage industry to define an AI-powered shopper measurement system using computer-vision for instore retail kiosks.
The goal was to create a computer-vision AI-powered shopper measurement system that could measure the real-time viewer demographics of digital content. This would enable targeting of product information appropriately to potential buyers.
The assessment results are summarized below.
Customer interviews have indicated that existing customers are likely to upgrade to the new demographic features and there is potential to increase market share significantly by recruiting new customers. Also, competitors are beginning to tout audience measurement capabilities.
Management is sensitive to protecting PII (Personally Identifiable Information) While the sensor is a digital camera, and the solution requires capturing images/video for information extraction, all images/videos must be protected with encryption and deleted within 24 hours. All analysis is to be done locally and no transfer of images or video will be made to the Internet.
Client dashboards and reporting was enhanced to include:
The applicable AI technique for the desired use cases is the extraction of information from camera images, specifically the identification of images of faces from images and classification of the faces by demographics.
In collaboration with the client we designed a set of experiments with an off-the-shelf image classifier package trained with the annotated images.
The goal was to exceed 80% accuracy. In practice we were able to achieve 95% accuracy in detecting faces, 90% accuracy in gender identification and 85% accuracy in age-range identification.
We recommended the following architecture choices:
We did a survey of data annotation vendors, conducted RFPs with potential vendors and contracted with an overseas vendor to receive over 100K images for evaluation.
A random set of images was examined by in house resources for validation.
Identified team gap expertise and presented internal training and external consultancy plan.
Identified data providers to source annotated training sets for the ML pipeline. An apt data architecture was prototyped and presented to the client.
Surveyed approaches for extracting the apt predictions. Created an experimental framework and set the goal of 80% accuracy. Ran a number of experiments to establish that this was achievable in practice.
AI MVP produced a working prototype of the product to validate with beta customers and to estimate the cloud resources needed to deliver the desired experience. Security issues were addressed, and we integrated media player metrics in the dashboard.
Further work was needed to help the client map the product roadmap to the new category.