Campus canteen crowd monitoring and occupancy level visualization using YOLOv8 / (Record no. 33842)

MARC details
000 -LEADER
fixed length control field 02148nam a2200301 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field CSPC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20261021141042.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 261021s2024 ph a b 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency CSPC
Language of cataloging eng
Transcribing agency CSPC
Description conventions rda
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) UTH BSCS M831c 2024
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Morata, Marri Grace,
Relator term author.
245 10 - TITLE STATEMENT
Title Campus canteen crowd monitoring and occupancy level visualization using YOLOv8 /
Statement of responsibility, etc. Marri Grace Morata, Christian Jay Tibi, and Adrian James Relles.
300 ## - PHYSICAL DESCRIPTION
Extent 133 leaves.
336 ## - CONTENT TYPE
Source rdacontent
Content type term text
337 ## - MEDIA TYPE
Source rdamedia
Media type term unmediated
338 ## - CARRIER TYPE
Source rdacarrier
Carrier type term volume
502 ## - DISSERTATION NOTE
Dissertation note Undergraduate Theses (BSCS) - Computer Science.
Name of granting institution Camarines Sur Polytechnic Colleges, 2024.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references.
520 3# - SUMMARY, ETC.
Summary, etc. 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
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Tibi, Christian Jay,
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Relles, Adrian James,
Relator term author.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Fortuno, Kaela Marie,
Relator term adviser.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Benitez, Ian P.,
Relator term chair, panelist.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Prianes, Freddie B.,
Relator term panelist.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Onesa, Rosel O.,
Relator term panelist.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Suppress in OPAC No
Koha item type Unpublished Materials
Classification part M831c
Item part BSCS
Call number prefix UTH
Call number suffix 2024
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Dewey Decimal Classification     Main Library Main Library Undergraduate Thesis Section 10/21/2026   UTH BSCS M831c 2024 007624UM 10/21/2026 1-1 10/21/2026 Unpublished Materials