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NVIDIA AI helps the retail industry deal with US$100B shrinkage problem

By Ken Wong - on 13 Jan 2023, 11:29am

NVIDIA AI helps the retail industry deal with US$100B shrinkage problem

NVIDIA AI is helping deal with a US$100B shrinkage issue. Image source: Unsplash.

The global retail industry has a US$100 billion shrinkage problem that apparently isn’t caused because its cold.

Shrinkage here refers to the loss of goods due to theft, damage and misplacement that significantly eats into retailers’ profits.

According to the National Retail Federation’s 2022 Retail Security Survey, an estimated 65% of shrinkage is due to theft with many retailers blaming current economic conditions contributing to the recent doubling of theft.

To make it easier for developers to quickly build and roll out applications designed to prevent theft, NVIDIA has announced three Retail AI Workflows which are available through the NVIDIA AI Enterprise software suite and built on its Metropolis Microservices, they include:

  • Retail Loss Prevention AI Workflow: The AI models within this workflow come pre-trained to recognize hundreds of products most frequently lost to theft — including meat, alcohol and laundry detergent — and to recognize them in the varying sizes and shapes they’re offered. With synthetic data generation from NVIDIA Omniverse, retailers and independent software vendors can customize and further train the models to hundreds of thousands of store products. The workflow is based on a state-of-the-art few-shot learning technique developed by NVIDIA Research which, combined with active learning, identifies and captures any new products scanned by customers and sales associates during checkout to ultimately improve model accuracy.
  • Multi-Camera Tracking AI Workflow: Delivers multi-target, multi-camera (MTMC) capabilities that allow application developers to more easily create systems that track objects across multiple cameras throughout the store. The workflow tracks objects and store associates across cameras and maintains a unique ID for each object. Objects are tracked through visual embeddings or appearance, rather than personal biometric information, to maintain full shopper privacy.
  • Retail Store Analytics Workflow: Uses computer vision to provide insights for store analytics, such as store traffic trends, counts of customers with shopping baskets, aisle occupancy and more via custom dashboards.

As the workflows are built on NVIDIA Metropolis microservices, a low- or no-code way of building AI applications, it provides the building blocks for developing complex AI workflows and allows them to rapidly scale into production-ready AI apps. The microservices also make it easier to integrate new offerings with legacy systems, such as point-of-sale systems.

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