全知:大型组织的分支监控系统

T. Jayasekara, Kalpani Omalka, Pamuditha Hewawelengoda, Chanuka Kanishka, Pradeepa Samarasinghe, L. Weerasinghe
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引用次数: 0

摘要

Omniscient是一种系统,它使大型组织的高层管理人员能够从总部本身远程监控和审查发生在分支机构的活动,并以每日、每周和每月的详细报告形式提供独家见解,了解员工的行为和表现。该系统进一步监察分行,并就任何可疑行为及客户在分行内的活动提供报告。Omniscient通过捕捉顾客的面部表情和分析他们在接受服务时的情绪来评估顾客的满意度。其中,员工面部识别模型和着装识别模型的准确率分别为90.90%和87.00%,员工活动检测模型的准确率为89.00%。顾客情感和杂项活动检测模型的准确率分别为91.50%和83.00%。上述所有程序都是通过系统分析全天获得的IP摄像机视频片段,使用CNN、OpenCV、Haar Cascade分类器、人脸识别、Dlib和Darknet等深度学习和现代视觉计算技术,尽可能准确地分析分支机构的工作效率和绩效而实现的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
OMNISCIENT: A Branch Monitoring System for Large-scale Organizations
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.
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