The global retail industry has a “Shrinkage” problem. In retail, shrinkage is defined as the loss of goods due to theft, damage and misplacement. According to the National Retail Federation, 65 percent of shrinkage is because of theft.
To make it easier for developers to build and roll out applications designed to prevent theft, NVIDIA announced three Retail AI Workflows, built on its Metropolis Microservices. They can be used as no-code or low-code building blocks for loss-prevention applications because they come pre-trained with images of the most-stolen products as well as software to plug into existing store applications for point-of-sale machines and object and product tracking across entire stores.
“Retail theft is growing due to macro-dynamics, and threatens to overwhelm the industry,” said Read Hayes, director of the Loss Prevention Research Council. “Businesses are now facing the reality that investment in loss-prevention solutions is a critical requirement.”
The NVIDIA Retail AI Workflows, which are available through the NVIDIA AI Enterprise software suite, 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 train the models to hundreds of thousands of store products.
- Multi-Camera Tracking AI Workflow – Delivers multi-target, multi-camera (MTMC) capabilities that allow application developers to 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.
The workflows are built on NVIDIA Metropolis Microservices, a low- or no-code way of building AI applications. The microservices provide the building blocks for developing complex AI workflows and allow them to rapidly scale into production-ready AI apps.
Developers can customize and extend these AI workflows, including by integrating their own models. The microservices make it easier to integrate new offerings with legacy systems, such as point-of-sale systems.
NVIDIA unveiled additional details of its Retail AI Workflows at the National Retail Federation Conference in New York.
