Customer Experience Management

Parandker, Sachin Ramesh; Lokku, Doji Samson

doi:10.1109/icsem.2012.14

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…On one hand, products under contracts should follow the compliant arrangement according to the contracts. They often appoint the number and position of product sets on a store shelf, which can further affect customer satisfaction and sales performance . On the other hand, manual inspection and management not only spend human resource but can also disturb shopping customers.…”

Section: Introductionmentioning

confidence: 99%

TemplateFree: product detection on retail store shelves

Sun

Zhang

Akashi

2019

IEEJ Transactions Elec Engng

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Product recognition has a significant role because of its benefits to the compliant arrangements of stores, which further affects commercial contracts, customer satisfaction, and sale achievement. Automatic recognition systems have been proposed owing to the current high cost of manual inspection by clerks. Because of the difficult collection of product images, the systems are commonly one-shot cases, in which the training data are actually template product images. However, despite the development of one-shot recognition, the systems rarely utilize the special characteristics of products on retail store shelves, and the frequent updating of templates is still challenging. Furthermore, we consider that product detection can be the basis of product recognition. In this article, instead of the present workflow, we propose a novel product detection system, named TemplateFree, which combines product segmentation and zero-shot learning. It detects products on retail store shelves by single store shelf images, that is, corresponding template product images are not necessary. TemplateFree concentrates on the characteristic that a store shelf can be segmented horizontally into layers and then vertically into products so that each product can be detected according to the segmentation. Double zero-shot deep learning frameworks are used to improve the segmentation. In experiments, TemplateFree achieves better results than the present method.

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Section: Introductionmentioning

confidence: 99%