This work investigates the new problem of image-based egocentric shopping cart localization in retail stores. The contribution of our work is two-fold. First, we propose a novel large-scale dataset for image-based egocentric shopping cart localization. The dataset has been collected using cameras placed on shopping carts in a large retail store. It contains a total of 19,531 image frames, each labelled with its six Degrees Of Freedom pose. We study the localization problem by analysing how cart locations should be represented and estimated, and how to assess the localization results. Second, we benchmark different image-based techniques to address the task. Specifically, we investigate two families of algorithms: Classic methods based on image retrieval and emerging methods based on regression. Experimental results show that methods based on image retrieval largely outperform regression-based approaches. We also point out that deep metric learning techniques allow to learn better visual representations w.r.t. Other architectures, and are useful to improve the localization results of both retrieval-based and regression-based approaches. Our findings suggest that deep metric learning techniques can help bridge the gap between retrieval-based and regression-based methods.

Egocentric Shopping Cart Localization

Spera, Emiliano;Furnari, Antonino;Battiato, Sebastiano;Farinella, Giovanni Maria
2018-01-01

Abstract

This work investigates the new problem of image-based egocentric shopping cart localization in retail stores. The contribution of our work is two-fold. First, we propose a novel large-scale dataset for image-based egocentric shopping cart localization. The dataset has been collected using cameras placed on shopping carts in a large retail store. It contains a total of 19,531 image frames, each labelled with its six Degrees Of Freedom pose. We study the localization problem by analysing how cart locations should be represented and estimated, and how to assess the localization results. Second, we benchmark different image-based techniques to address the task. Specifically, we investigate two families of algorithms: Classic methods based on image retrieval and emerging methods based on regression. Experimental results show that methods based on image retrieval largely outperform regression-based approaches. We also point out that deep metric learning techniques allow to learn better visual representations w.r.t. Other architectures, and are useful to improve the localization results of both retrieval-based and regression-based approaches. Our findings suggest that deep metric learning techniques can help bridge the gap between retrieval-based and regression-based methods.
2018
9781538637883
1707
File in questo prodotto:
File Dimensione Formato  
Egocentric Shopping Cart Localization..pdf

solo gestori archivio

Descrizione: Articolo principale
Tipologia: Versione Editoriale (PDF)
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 4.9 MB
Formato Adobe PDF
4.9 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/365580
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 13
  • ???jsp.display-item.citation.isi??? 7
social impact