![]() ![]() Since this solution is tiered, it's important to think about how to deal with networking or power failures. For more information, see Microsoft Azure Well-Architected Framework. These considerations implement the pillars of the Azure Well-Architected Framework, which is a set of guiding tenets that can be used to improve the quality of a workload. The solution provides an unobtrusive, privacy-friendly way to determine customer demographics, loyalty, and reactions to store displays and products. The solution also doesn't require stores to have enough bandwidth to stream video from all their cameras, to the cloud for analysis. With this scenario, you don't have to place staff in every section, and you don't need a team of analysts to review all of a store's camera footage. The solution generates insights from real world actions, by using Azure, Azure Stack Hub, and the Custom Vision AI Dev Kit. This solution outlines an AI-based footfall detection architecture for analyzing visitor traffic in retail stores. However, there are other compute options, such as a custom app that runs on Azure App Service or Azure Kubernetes Service (AKS) Engine. It emits anonymized data to an Event Hubs cluster that's located in Azure.Īn Azure Function that's running on Azure Stack Hub is a great compute option. An Azure Function running on Azure Stack Hub receives input from Blob storage and manages the interactions with the Face API. Images captured from the AI Dev Kit are uploaded to Azure Stack Hub's Blob storage. The Azure Cognitive Services RP, with Face API containers, provides demographic, emotion, and unique visitor detection. The AKS RP with an AKS-engine cluster is deployed into Azure Stack Hub, to provide a scalable, resilient engine that runs the Face API container.
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