Tackling US$100b retail problem with AI

The retail industry has a massive problem. Globally, it faces a US$100 billion shrinkage due to loss of goods from theft, damage and misplacement.

According to the National Retail Federation’s 2022 Retail Security Survey, a significant portion (65 percent) of this is due to theft. And theft cases has more than doubled because of rising prices of food and other essentials.

“Retail theft is growing due to macro-dynamics, and threatens to overwhelm the industry. Businesses are now facing the reality that investment in loss-prevention solutions is a critical requirement,” said Read Hayes, Director of Loss Prevention Research Council.

NVIDIA has rolled out three Retail AI Workflows, built on its Metropolis microservices, to help mitigate thefts. To make it simple for retailers to adopt, these workflows 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.

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 pretrained to recognise hundreds of products most frequently lost to theft — including meat, alcohol and laundry detergent — and to recognise them in the varying sizes and shapes. With synthetic data generation from NVIDIA Omniverse, retailers and independent software vendors can customise and further train the models to hundreds of thousands of store products.
• Multi-Camera Tracking AI Workflow: Delivers multi-target, multi-camera 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.

They are built on NVIDIA Metropolis microservices, a low- or no-code way of building AI applications. Developers can easily customise and extend these AI workflows, including by integrating their own models. The microservices also make it easier to integrate new offerings with legacy systems, such as point-of-sale systems.