000			03201nam a22003377a 4500
003			OSt
005			20250313154032.0
008			250313b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH QA 76.9 _bA76 2024
100			_aArnosa, Daniela C. _eauthor
245			_aDevelopment of response system using cctv with image processing/ _cDaniela C. Arnosa, Johnrey A. Bito-onon, Adrian B. Calipdan, Isa Red G. Carpio and Jaydel E. Esquilona.--
260			_aTechnological University of the Philippines, Manila. _c2024
300			_ax, 150 pages. _c29cm
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege of Industrial Technology.-- _bBachelor of Engineering Technology major in Computer Engineering Technology: _cTechnological University of the Philippines, Manila. _d2024
504			_aIncludes bibliographic references and index.
520			_aThe relationship between image processing methods and object detection models including You Only Look Once version 4 also known as YOLOv4 has drastically improved real-time count monitoring capabilities. The accurate object detection features and high-processing speed of YOLOv4 provide an ideal solution for safety-enhancing systems operating in dynamic environments. The proponents aim to develop a project that enhance crowd counting using CCTV with image processing that has features such as audio output and real-time crowd monitoring. The system is a web application using python language with OpenCV. It analyzes crowd density by processing IP camera feeds and as a data gatherer. YOLOv4 will process the data gathered to identify if it aligns with the attributes of a human. Once the data exceeded the limit for each CCTV, an audio exit guidance will play during heavy crowd situations or "extreme" crowd conditions. The system uses modern technologies to modernize traditional operations so that it delivers quick crowd management solutions for crisis situations. The system was evaluated using the ISO 25010 standards focusing on functionality, usability, reliability, and performance efficiency through survey questionnaire with 15 Professionals and 20 end-users and students as respondents. The project achieved an overall weighted mean of 4.78 or “Excellent” describe as in functionality, usability, reliability, and performance efficiency aspects. This result indicates that the system is effective and efficient in terms of crowd monitoring, analyzing data gathered through the system, and effectively guided crowds when they reach high density or the “extreme” level. The system enhances the safety and provides an essential upgrade for traditional crowd counting methods and response. Key words: IP Camera, CCTV Image Processing, YOLOv4, Audio.
650			_aComputer Engineering Technology
650			_aCCTV
650			_aImage Processing
650			_aResponse System
700			_aBito-onon, Johnrey A. _eauthor
700			_aCalipdan, Adrian B. _eauthor
700			_aCarpio, Isa Red G. _eauthor
700			_aEsquilona, Jaydel E. _eauthor
942			_2lcc _cBTH CIT _n0
999			_c29486 _d29486