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Campus canteen crowd monitoring and occupancy level visualization using YOLOv8 / Marri Grace Morata, Christian Jay Tibi, and Adrian James Relles.

By:

Morata, Marri Grace [author.]

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Material type: Text

TextDescription: 133 leavesContent type:

text

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unmediated

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volume

Dissertation note: Undergraduate Theses (BSCS) - Computer Science. Camarines Sur Polytechnic Colleges, 2024. Abstract: This study introduces a real-time Crowd Monitoring and Occupancy Level System de signed for the CSPC campus canteen. The primary objective is to address the challenges of traditional crowd monitoring methods by implementing the YOLOv8 algorithm for au tomated detection and counting of individuals. The system processes video streams from a designated area, quantifies the detected crowd, and categorizes the occupancy level as less crowded, crowded, or overcrowded. The prototype was evaluated for its accuracy and func tionality in the campus canteen, demonstrating high efficiency in detecting crowds with a precision score of 97.20%, recall of 96.08%, and accuracy of 93.50%. The results indicate that the system successfully automates crowd monitoring and occupancy level detection, making it suitable for applications in public safety, resource management, and planning. Future work could explore enhancements such as multi-camera integration, server integra tion, and predictive analytics for crowd movement trends

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Item type	Current library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Unpublished Materials	Main Library	Undergraduate Thesis Section	UTH BSCS M831c 2024 (Browse shelf(Opens below))	1-1	Not for loan		007624UM

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Previous	UTH BSCS M672r 2024 Real-time local weather station using gradient boosting /	UTH BSCS M815c 2021 Classification of broiler chicks using image processing and fuzzy logic	UTH BSCS M815i 2017 iData: Mining CSPC databank for performance analysis/	UTH BSCS M831c 2024 Campus canteen crowd monitoring and occupancy level visualization using YOLOv8 /	UTH BSCS N113e 2018 Emergency facility navigation using genetic algorithm/	UTH BSCS N127c 2024 Comparative analysis of image deblurring using deblurgan for sharpening selected and entire image region /	UTH BSCS N149e 2017 Electronic signature using cubic bezier spline interpolation algorithm/	Next

Undergraduate Theses (BSCS) - Computer Science. Camarines Sur Polytechnic Colleges, 2024.

Includes bibliographical references.

This study introduces a real-time Crowd Monitoring and Occupancy Level System de signed for the CSPC campus canteen. The primary objective is to address the challenges of traditional crowd monitoring methods by implementing the YOLOv8 algorithm for au tomated detection and counting of individuals. The system processes video streams from a designated area, quantifies the detected crowd, and categorizes the occupancy level as less crowded, crowded, or overcrowded. The prototype was evaluated for its accuracy and func tionality in the campus canteen, demonstrating high efficiency in detecting crowds with a precision score of 97.20%, recall of 96.08%, and accuracy of 93.50%. The results indicate that the system successfully automates crowd monitoring and occupancy level detection, making it suitable for applications in public safety, resource management, and planning. Future work could explore enhancements such as multi-camera integration, server integra tion, and predictive analytics for crowd movement trends

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