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https://rda.sliit.lk/handle/123456789/1940
Title: | OMNISCIENT: A Branch Monitoring System for Large-scale Organizations |
Authors: | Jayasekara, T Omalka, K Hewawelengoda, P Kanishka, C Samarasinghe, P Weerasinghe, L |
Keywords: | OMNISCIENT Branch Monitoring Monitoring System Large-scale Organizations |
Issue Date: | 10-Dec-2020 |
Publisher: | IEEE |
Citation: | T. 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. |
Series/Report no.: | 2020 2nd International Conference on Advancements in Computing (ICAC);Vol 1 Pages 440-445 |
Abstract: | Omniscient 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. |
URI: | http://rda.sliit.lk/handle/123456789/1940 |
ISBN: | 978-1-7281-8412-8 |
Appears in Collections: | Department of Information Technology-Scopes Research Papers - SLIIT Staff Publications Research Publications -Dept of Information Technology |
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