We consider the task of localizing shopping carts in a retail store from egocentric images. Addressing this task allows to infer information on the behavior of the customers to understand how they move in the store and what they pay more attention to. To study the problem, we propose a large dataset of images collected in a real retail store

EgoCart: a Benchmark Dataset for Large-Scale Indoor Image-Based Localization in Retail Stores

Spera, Emiliano;Furnari, Antonino;Battiato, Sebastiano;Farinella, Giovanni Maria
2019

Abstract

We consider the task of localizing shopping carts in a retail store from egocentric images. Addressing this task allows to infer information on the behavior of the customers to understand how they move in the store and what they pay more attention to. To study the problem, we propose a large dataset of images collected in a real retail store
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/377792
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