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AI-based machine vision for retail self-checkout system

Rigner, Anton LU (2019) In Master's Theses in Mathematical Sciences FMAM05 20191
Mathematics (Faculty of Engineering)
Abstract
In recent years advances in computing power, availability of large
annotated datasets and AI algorithms have enabled the rise of reliable
object identification and tracking. This thesis describes the development
and implementation considerations of a system for object detection in
the retail industry. This project have been conducted as a collaboration
between ETH Zürich and start-up company AI Retailing Systems, who
wants to automate parts of the retailing experience, namely the checkout
procedure in a retail store. A data set of images with corresponding
bounding boxes and pixel-segmentations has been gathered, consisting
of ten Swiss retail products. Relevant theory is discussed and three state
of the art neural network... (More)
In recent years advances in computing power, availability of large
annotated datasets and AI algorithms have enabled the rise of reliable
object identification and tracking. This thesis describes the development
and implementation considerations of a system for object detection in
the retail industry. This project have been conducted as a collaboration
between ETH Zürich and start-up company AI Retailing Systems, who
wants to automate parts of the retailing experience, namely the checkout
procedure in a retail store. A data set of images with corresponding
bounding boxes and pixel-segmentations has been gathered, consisting
of ten Swiss retail products. Relevant theory is discussed and three state
of the art neural network architectures are reviewed and evaluated for the
specific application and dataset. The thesis concludes with a discussion
of the main challenges for this type of solution, a recommendation for
the object detection model to be used and pointers for future work. (Less)
Popular Abstract (Swedish)
Vi har alla stått alldeles för länge i en kö för att handla den där chipspåsen, lunchlådan eller lördagsgodiset. Att handla mat är ett måste för oss alla, och de flesta av oss föredrar att få det överstökat så snabbt som möjligt. Detaljhandeln erbjuder snabbkassor, men nu är det dags för nästa steg, helt automatiska lösningar som använder den senaste tekniken inom datorseende och artificiell intelligens.
Please use this url to cite or link to this publication:
author
Rigner, Anton LU
supervisor
organization
alternative title
AI-baserat datorseende för automatiserad betalningsprocedur i detaljhandeln
course
FMAM05 20191
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Object detection, Retail, Automatic Checkout, Artificial Intelligence, Deep Learning, Computer Vision
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3387-2019
ISSN
1404-6342
other publication id
2019:E33
language
English
id
8985308
date added to LUP
2019-07-16 13:38:03
date last changed
2019-12-30 03:39:07
@misc{8985308,
  abstract     = {{In recent years advances in computing power, availability of large
annotated datasets and AI algorithms have enabled the rise of reliable
object identification and tracking. This thesis describes the development
and implementation considerations of a system for object detection in
the retail industry. This project have been conducted as a collaboration
between ETH Zürich and start-up company AI Retailing Systems, who
wants to automate parts of the retailing experience, namely the checkout
procedure in a retail store. A data set of images with corresponding
bounding boxes and pixel-segmentations has been gathered, consisting
of ten Swiss retail products. Relevant theory is discussed and three state
of the art neural network architectures are reviewed and evaluated for the
specific application and dataset. The thesis concludes with a discussion
of the main challenges for this type of solution, a recommendation for
the object detection model to be used and pointers for future work.}},
  author       = {{Rigner, Anton}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{AI-based machine vision for retail self-checkout system}},
  year         = {{2019}},
}