000			02148nam a2200301 i 4500
003			CSPC
005			20261021141042.0
008			261021s2024 ph a b 000 0 eng d
040			_aCSPC _beng _cCSPC _erda
090			_aUTH BSCS M831c 2024
100	1		_aMorata, Marri Grace, _eauthor.
245	1	0	_aCampus canteen crowd monitoring and occupancy level visualization using YOLOv8 / _cMarri Grace Morata, Christian Jay Tibi, and Adrian James Relles.
300			_a133 leaves.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
502			_aUndergraduate Theses (BSCS) - Computer Science. _cCamarines Sur Polytechnic Colleges, 2024.
504			_aIncludes bibliographical references.
520	3		_aThis 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
700	1		_aTibi, Christian Jay, _eauthor.
700	1		_aRelles, Adrian James, _eauthor.
720			_aFortuno, Kaela Marie, _eadviser.
720			_aBenitez, Ian P., _echair, panelist.
720			_aPrianes, Freddie B., _epanelist.
720			_aOnesa, Rosel O., _epanelist.
942			_2ddc _n0 _cUM _hM831c _iBSCS _kUTH _m2024
999			_c33842 _d33842