Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1940
Full metadata record
DC FieldValueLanguage
dc.contributor.authorJayasekara, T-
dc.contributor.authorOmalka, K-
dc.contributor.authorHewawelengoda, P-
dc.contributor.authorKanishka, C-
dc.contributor.authorSamarasinghe, P-
dc.contributor.authorWeerasinghe, L-
dc.date.accessioned2022-04-07T03:51:18Z-
dc.date.available2022-04-07T03:51:18Z-
dc.date.issued2020-12-10-
dc.identifier.citationT. Jayasekara, K. Omalka, P. Hewawelengoda, C. Kanishka, P. Samarasinghe and L. Weerasinghe, "OMNISCIENT: A Branch Monitoring System for Large-scale Organizations," 2020 2nd International Conference on Advancements in Computing (ICAC), 2020, pp. 440-445, doi: 10.1109/ICAC51239.2020.9357271.en_US
dc.identifier.isbn978-1-7281-8412-8-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1940-
dc.description.abstractOmniscient is a system that enables higher-level management of massive organizations to remotely monitor and scrutinize the activities that take place in the branches from the head office itself by providing exclusive insight in the form of detailed reports on the employees' behaviour and performance daily, weekly and monthly. The system further monitors the branch and provides reports on any suspicious behaviour and also on the customers' activity within the branch premises. Omniscient rates the customer's level of satisfaction by capturing the customer's facial expressions and analyzing their emotions while they are being served. The employee face and dress recognition models have accuracies of 90.90% and 87.00% respectively while, employee activity detection has an accuracy of 89.00%. Customer emotion and miscellaneous activities detection models have the accuracies of 91.50% and 83.00% respectively. All of the aforementioned procedures were made possible by systematically analyzing the IP camera video footage obtained throughout the day to analyze the work productivity and performance of the branch as accurately as possible using deep learning and modern visual computing techniques like CNN, OpenCV, Haar Cascade classifier, face recognition, Dlib and Darknet.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2020 2nd International Conference on Advancements in Computing (ICAC);Vol 1 Pages 440-445-
dc.subjectOMNISCIENTen_US
dc.subjectBranch Monitoringen_US
dc.subjectMonitoring Systemen_US
dc.subjectLarge-scaleen_US
dc.subjectOrganizationsen_US
dc.titleOMNISCIENT: A Branch Monitoring System for Large-scale Organizationsen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC51239.2020.9357271en_US
Appears in Collections:Department of Information Technology-Scopes
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

Files in This Item:
File Description SizeFormat 
OMNISCIENT_A_Branch_Monitoring_System_for_Large-scale_Organizations.pdf
  Until 2050-12-31
858.49 kBAdobe PDFView/Open Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.