In-store customer traffic and path monitoring in small-scale supermarket using UWB-based localization and SSD-based detection

Nowadays, retailers are embracing the Internet of Things as the latest technology to drive superior customer experience. Leverage data sources from sensors, beacons and mobile devices to identify and analyze in-store customer shopping behavior. With this motivation, this study implemented an in-stor...

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Main Authors: Alipio, Melchizedek Ibarrientos, Penalosa, Kathlyn Mae T., Unida, Julioh Roscoe C.
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Published: Animo Repository 2020
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/3489
https://animorepository.dlsu.edu.ph/context/faculty_research/article/4491/type/native/viewcontent/s12652_020_02236_z.html
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-44912022-07-07T03:18:08Z In-store customer traffic and path monitoring in small-scale supermarket using UWB-based localization and SSD-based detection Alipio, Melchizedek Ibarrientos Penalosa, Kathlyn Mae T. Unida, Julioh Roscoe C. Nowadays, retailers are embracing the Internet of Things as the latest technology to drive superior customer experience. Leverage data sources from sensors, beacons and mobile devices to identify and analyze in-store customer shopping behavior. With this motivation, this study implemented an in-store customer traffic and path monitoring system for supermarket using image processing and object detection. the system utilized the ultra-wideband indoor positioning technique to monitor the customer shopping path and the single shot multibox detection technique to monitor the real-time customer traffic. The customer monitoring system was implemented and evaluated in an actual small-scale supermarket. Results showed that the detection model prediction score and the traffic counting both obtained an accuracy score of 99%. In addition, the localization system achieved the minimum error difference of 9.73% for x coordinate and 3.86% for y coordinate between pre-determined positions and the actual anchor position readings. Furthermore, the system successfully generated the most frequent path and the total customer traffic of the day. In the future, this work can aid retail owners make better choices, run businesses more efficiently, and deliver improved customer service. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature. 2020-01-01T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/3489 info:doi/10.1007/s12652-020-02236-z https://animorepository.dlsu.edu.ph/context/faculty_research/article/4491/type/native/viewcontent/s12652_020_02236_z.html Faculty Research Work Animo Repository Indoor positioning systems (Wireless localization) Supermarkets—Automation Image processing Ultra-wideband communication systems Electrical and Computer Engineering Electrical and Electronics
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Indoor positioning systems (Wireless localization)
Supermarkets—Automation
Image processing
Ultra-wideband communication systems
Electrical and Computer Engineering
Electrical and Electronics
spellingShingle Indoor positioning systems (Wireless localization)
Supermarkets—Automation
Image processing
Ultra-wideband communication systems
Electrical and Computer Engineering
Electrical and Electronics
Alipio, Melchizedek Ibarrientos
Penalosa, Kathlyn Mae T.
Unida, Julioh Roscoe C.
In-store customer traffic and path monitoring in small-scale supermarket using UWB-based localization and SSD-based detection
description Nowadays, retailers are embracing the Internet of Things as the latest technology to drive superior customer experience. Leverage data sources from sensors, beacons and mobile devices to identify and analyze in-store customer shopping behavior. With this motivation, this study implemented an in-store customer traffic and path monitoring system for supermarket using image processing and object detection. the system utilized the ultra-wideband indoor positioning technique to monitor the customer shopping path and the single shot multibox detection technique to monitor the real-time customer traffic. The customer monitoring system was implemented and evaluated in an actual small-scale supermarket. Results showed that the detection model prediction score and the traffic counting both obtained an accuracy score of 99%. In addition, the localization system achieved the minimum error difference of 9.73% for x coordinate and 3.86% for y coordinate between pre-determined positions and the actual anchor position readings. Furthermore, the system successfully generated the most frequent path and the total customer traffic of the day. In the future, this work can aid retail owners make better choices, run businesses more efficiently, and deliver improved customer service. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
format text
author Alipio, Melchizedek Ibarrientos
Penalosa, Kathlyn Mae T.
Unida, Julioh Roscoe C.
author_facet Alipio, Melchizedek Ibarrientos
Penalosa, Kathlyn Mae T.
Unida, Julioh Roscoe C.
author_sort Alipio, Melchizedek Ibarrientos
title In-store customer traffic and path monitoring in small-scale supermarket using UWB-based localization and SSD-based detection
title_short In-store customer traffic and path monitoring in small-scale supermarket using UWB-based localization and SSD-based detection
title_full In-store customer traffic and path monitoring in small-scale supermarket using UWB-based localization and SSD-based detection
title_fullStr In-store customer traffic and path monitoring in small-scale supermarket using UWB-based localization and SSD-based detection
title_full_unstemmed In-store customer traffic and path monitoring in small-scale supermarket using UWB-based localization and SSD-based detection
title_sort in-store customer traffic and path monitoring in small-scale supermarket using uwb-based localization and ssd-based detection
publisher Animo Repository
publishDate 2020
url https://animorepository.dlsu.edu.ph/faculty_research/3489
https://animorepository.dlsu.edu.ph/context/faculty_research/article/4491/type/native/viewcontent/s12652_020_02236_z.html
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