Arnosa, Daniela C. <br/><br/>
Development of response system using cctv with image processing/ 
Daniela C. Arnosa, Johnrey A. Bito-onon, Adrian B. Calipdan, Isa Red G. Carpio and Jaydel E. Esquilona.-- 
 - Technological University of the Philippines, Manila.  2024 
 - x, 150 pages.  29cm 
<br/><br/> Bachelor's thesis 
<br/><br/>College of Industrial Technology.-- 
<br/><br/>Includes bibliographic references and index. 
<br/><br/> The relationship between image processing methods and object detection models including<br/>You Only Look Once version 4 also known as YOLOv4 has drastically improved real-time<br/>count monitoring capabilities. The accurate object detection features and high-processing<br/>speed of YOLOv4 provide an ideal solution for safety-enhancing systems operating in<br/>dynamic environments. The proponents aim to develop a project that enhance crowd<br/>counting using CCTV with image processing that has features such as audio output and<br/>real-time crowd monitoring. The system is a web application using python language with<br/>OpenCV. It analyzes crowd density by processing IP camera feeds and as a data gatherer.<br/>YOLOv4 will process the data gathered to identify if it aligns with the attributes of a<br/>human. Once the data exceeded the limit for each CCTV, an audio exit guidance will play<br/>during heavy crowd situations or "extreme" crowd conditions. The system uses modern<br/>technologies to modernize traditional operations so that it delivers quick crowd<br/>management solutions for crisis situations. The system was evaluated using the ISO 25010<br/>standards focusing on functionality, usability, reliability, and performance efficiency<br/>through survey questionnaire with 15 Professionals and 20 end-users and students as<br/>respondents. The project achieved an overall weighted mean of 4.78 or “Excellent”<br/>describe as in functionality, usability, reliability, and performance efficiency aspects. This<br/>result indicates that the system is effective and efficient in terms of crowd monitoring,<br/>analyzing data gathered through the system, and effectively guided crowds when they reach<br/>high density or the “extreme” level. The system enhances the safety and provides an<br/>essential upgrade for traditional crowd counting methods and response.<br/>Key words: IP Camera, CCTV Image Processing, YOLOv4, Audio. 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Computer Engineering Technology<br/>CCTV<br/>Image Processing <br/>Response System
<br/><br/><label>LC Class. No.: </label>BTH QA 76.9   / A76 2024