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How is computer vision used in cashier-less stores like Amazon Go?

Computer vision powers cashier-less stores through sophisticated tracking and identification systems. Hundreds of cameras positioned throughout the store create a three-dimensional understanding of the space. As customers enter (typically via app check-in or credit card scan), the system identifies and tracks their movement throughout their shopping journey. The AI tracks 'pick' and 'put-back' events: when a customer takes an item from a shelf, computer vision identifies which product was selected and associates it with that customer's virtual cart. If they return it to the shelf, the system removes it from the cart. Weight sensors, RFID tags, and other technologies complement vision systems for accuracy. Deep learning models trained on millions of shopping interactions handle challenging scenarios: multiple customers reaching for items simultaneously, products being examined then returned, occluded views, and varying lighting conditions. The system achieves 95%+ accuracy in product identification and customer association. Benefits include: eliminated checkout lines, reduced labor costs (though staff still stock shelves and assist customers), improved customer experience through frictionless shopping, detailed analytics on shopping behavior and store flow, and prevention of traditional checkout-related shrink. The technology requires significant upfront investment but delivers long-term operational savings and customer satisfaction improvements.