Скачать или смотреть Enhancing Crowd Monitoring with AI: Real-Time Detection Using OpenCV, MTCNN & TensorFlow

This study explores the integration of OpenCV, MTCNN, and TensorFlow to develop an intelligent crowd density monitoring system. The system utilizes OpenCV's robust real-time image processing, MTCNN's precise facial detection, and TensorFlow's scalable machine learning capabilities to accurately identify faces and measure crowd density in specific areas. The implementation leverages video footage from the 2024 Jakarta gubernatorial election, showcasing the system's ability to handle real-world, high-density crowd scenarios. Applications span various sectors, from enhancing public safety and operational efficiency in workplaces to improving visitor experiences in public venues. The system also paves the way for futuristic smart city solutions, offering automated crowd management and predictive insights to optimize urban resource allocation. This innovation demonstrates the potential of AI-driven technologies in transforming surveillance and resource management in dynamic environments.

Keywords:
Crowd Density Monitoring, Face Detection, OpenCV, MTCNN, TensorFlow, Intelligent Surveillance, Smart Cities, Real-Time Analysis, Machine Learning, Public Safety, Resource Optimization, Predictive Insights, Jakarta Gubernatorial Election 2024.

project file: https://drive.google.com/file/d/1gksL...

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