Computer vision based planogram compliance evaluation

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http://urn.fi/URN:NBN:fi:hulib-202106092585
Titel: Computer vision based planogram compliance evaluation
Författare: Laitala, Julius
Medarbetare: Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta
University of Helsinki, Faculty of Science
Helsingfors universitet, Matematisk-naturvetenskapliga fakulteten
Utgivare: Helsingin yliopisto
Datum: 2021
Språk: eng
Permanenta länken (URI): http://urn.fi/URN:NBN:fi:hulib-202106092585
http://hdl.handle.net/10138/330784
Nivå: pro gradu-avhandlingar
Utbildningsprogram: Tietojenkäsittelytieteen maisteriohjelma
Master's Programme in Computer Science
Magisterprogrammet i datavetenskap
Studieinriktning: Algoritmit
Algorithms
Algoritmer
Abstrakt: Arranging products in stores according to planograms, optimized product arrangement maps, is important for keeping up with the highly competitive modern retail market. The planograms are realized into product arrangements by humans, a process which is prone to mistakes. Therefore, for optimal merchandising performance, the planogram compliance of the arrangements needs to be evaluated from time to time. We investigate utilizing a computer vision problem setting – retail product detection – to automate planogram compliance evaluation. We introduce the relevant problems, the state-of- the-art approaches for solving them and background information necessary for understanding them. We then propose a computer vision based planogram compliance evaluation pipeline based on the current state of the art. We build our proposed models and algorithms using PyTorch, and run tests against public datasets and an internal dataset collected from a large Nordic retailer. We find that while the retail product detection performance of our proposed approach is quite good, the planogram compliance evaluation performance of our whole pipeline leaves a lot of room for improvement. Still, our approach seems promising, and we propose multiple ways for improving the performance enough to enable possible real world utility. The code used for our experiments and the weights for our models are available at https://github.com/laitalaj/cvpce
Subject: computer vision
object detection
retail product detection
planogram compliance


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