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.--

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Arnosa, Daniela C [author]

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Material type: Text

TextPublication details: Technological University of the Philippines, Manila. 2024Description: x, 150 pages. 29cmContent type:

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BTH QA 76.9 A76 2024

Dissertation note: College of Industrial Technology.-- Bachelor of Engineering Technology major in Computer Engineering Technology: Technological University of the Philippines, Manila. 2024 Summary: The 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.

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Holdings
Item type	Current library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Bachelor's Thesis CIT	TUP Manila Library	Thesis Section-2nd floor	BTH QA 76.9 A76 2024 (Browse shelf(Opens below))	c.1	Not for loan		BTH0005896

Bachelor's thesis

College of Industrial Technology.-- Bachelor of Engineering Technology major in Computer Engineering Technology: Technological University of the Philippines, Manila. 2024

Includes bibliographic references and index.

The 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.

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