Saidatul Ardeenawatie Awang
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Preferred name
Saidatul Ardeenawatie Awang
Official Name
Saidatul Ardeenawatie, Awang
Alternative Name
Saidatul, Ardeenawatie
Awang, S. A.
Awang, Saidatul Ardeenawatie
Awang, Saidatul Ardeenaawatie
Awang, Saidatul Ardeenawatie Binti
Awang, Saidatul Ardeenawatiebinti
Saidatul, A.
Main Affiliation
Scopus Author ID
57205231792
Researcher ID
CCJ-6771-2022

Research Output

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  • Publication
    Traffic density estimation and mapping using IP-CCTV networks: a campus-based approach
    (AIP Publishing, 2023)
    Roy Francis Navea
    ;
    John Carl Bautista
    ;
    Adrian Giuseppe Francis Fernan
    ;
    Zendrel Gacuya
    ;
    Saidatul Ardeenawatie Awang
    Closed Circuit Television (CCTV) systems are widely used in monitoring and security surveillance applications to assess status and implement measures necessary to address problems and concerns. CCTV nowadays are visible in roads for monitoring and analyzing traffic behavior and conditions. Multiple cameras are utilized to capture the different angles of the road. This is useful in improving traffic management systems, determination of road traffic density, accident reviews, and in some advanced applications, contactless apprehension. Small to medium scale community areas like industrial parks, villages, hospitals, and even academic campuses require traffic monitoring systems. In this study, a network of IP-CCTV cameras was designed to capture vehicular movement, density, and road condition in a campus setting. The network is composed of eight IP-CCTV cameras processed by four Raspberry Pi computer boards in a 2:1 camera-to-computer ratio. A graphical user interface displays the video feed of the cameras, time customizable traffic report, and the road map visualization and notifications. All computer boards can send and receive data and can create visual traffic maps displayed in the user interface. Color-coding is used in the road segments to indicate light, moderate, or heavy traffic conditions. The vehicle detection accuracy of the system is 93% while its status notification accuracy is 84%. In a campus-based application, especially those with medical and health research institutes, this model suffices its requirements in monitoring, analyzing, basis for emergency rerouting, and improvements in traffic management.
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