Chee Jia Hong, Chee Jia Hong and Mazlan, Muhammad Hazli (2023) Development of Automated People Counting System using Object Detection and Tracking. Development of Automated People Counting System using Object Detection and Tracking, 19 (6). pp. 18-30.
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Abstract
The emergence of automation in the current economic trend promotes the usage of computer vision systems in various applications. Counting people in a specified area or on the street can bring many benefits in terms of security and marketing. The people counting system is one of the applications that utilize the computer vision system to count people with higher reliability and accuracy. Thus, this project is to develop an offline automated people counting system based on captured video file input using MATLAB software and a notification system to update and send notifications about the number of occupants in a target area using ThingSpeak. For project development, simulation and development of coding for object detection that involves deep learning approach, object tracking and counting, and development of notification system have been done. Three videos were taken to be used for three trials to evaluate the functionality and performance of the developed system. Based on the results and analysis, the system can perform people detection, people tracking and people counting on the recorded input videos with high accuracy of 94.45%, visualize the data on the ThingSpeak platform and send notifications through Twitter.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | people counting, MATLAB, ThingSpeak, deep learning, object detection, object tracking, Mask R-CNN |
| Subjects: | T Technology > TJ Mechanical engineering and machinery > TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) |
| Divisions: | Faculty of Electrical and Electronic Engineering > Department of Electrical Engineering |
| Depositing User: | Mr. Mohamad Zulkhibri Rahmad |
| Date Deposited: | 11 Oct 2023 07:21 |
| Last Modified: | 11 Oct 2023 07:21 |
| URI: | http://eprints.uthm.edu.my/id/eprint/10082 |
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