Using image analytics to monitor retail store shelves

Marder, Mattias; Harary, Sivan; Ribak, Amnon; Tzur, Yochay; Alpert, Sharon; Tzadok, Asaf

doi:10.1147/jrd.2015.2394513

Cited by 34 publications

(25 citation statements)

References 23 publications

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“…As regards comparative evaluation with respect to previous work, it is worth highlighting that the only work addressing exactly the same task as ours is [19], but neither their dataset nor their implementation are publicly available. Indeed, their system is quite complex and tailored for their specific use case so it would have been unfair to reproduce their results on our dataset by our own implementation based only on their paper.…”

Section: Resultsmentioning

confidence: 99%

“…Accordingly, our proposed pipeline can work effectively in realistic scenarios in which just one model image per product and the planogram are available and the given image is not a priory localized with respect to the aisle. Unfortunately, a quantitative comparison to the most relevant previous work [19] is not feasible, as the authors used a dataset that cannot be make public. Nonetheless, their dataset seems comparable to ours in terms of number of different products and instances.…”

Section: Discussionmentioning

confidence: 99%

“…Marder et al in [19] addressed our exact same problem of checking planogram compliance through computer vision. Their approach relies on detecting and matching SURF features [6] followed by visual and logical disambiguation between similar products.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Product Recognition in Store Shelves as a Sub-Graph Isomorphism Problem

Tonioni

Stefano

2017

Lecture Notes in Computer Science

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The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy. However, verifying compliance of real shelves to the ideal layout is a costly task routinely performed by the store personnel. In this paper, we propose a computer vision pipeline to recognize products on shelves and verify compliance to the planned layout. We deploy local invariant features together with a novel formulation of the product recognition problem as a sub-graph isomorphism between the items appearing in the given image and the ideal layout. This allows for auto-localizing the given image within the aisle or store and improving recognition dramatically. 1 missing or misplaced items. Key to our approach is a novel formulation of the problem as a sub-graph isomorphism between the product detected in the given image and those that should ideally be found therein given the planogram. Accordingly, our pipeline relies on a standard feature-based object recognition step, followed by the novel graph-based consistency check and a final localized image search to improve the overall product recognition rate.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Product Recognition in Store Shelves as a Sub-Graph Isomorphism Problem

Tonioni

Stefano

2017

Lecture Notes in Computer Science

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“…Marder et al . propose to monitor retail store shelves by image analysis, which apply the state‐of‐the‐art object detection method at that time. Varol et al .…”

Section: Related Workmentioning

confidence: 99%

“…The performances of these methods rely greatly on the performance of the corresponding object detection algorithm. Although video data and additional information (e.g. words on products and planogram ) are then leveraged to product recognition, the scarcity of related journal articles and patents, as well as the inadequacy of product species in experiments, show that product recognition is at a prototype stage.…”

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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Alternative Data in FinTech and Business Intelligence

Cong

Zhang

2021

The Palgrave Handbook of FinTech and Blockchain

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Using image analytics to monitor retail store shelves

Cited by 34 publications

References 23 publications

Product Recognition in Store Shelves as a Sub-Graph Isomorphism Problem

Product Recognition in Store Shelves as a Sub-Graph Isomorphism Problem

TemplateFree: product detection on retail store shelves

Alternative Data in FinTech and Business Intelligence

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